ABSTRACT:

MapReduce job, we consider to aggregate data with the same keys before sending them to remote reduce tasks. Although a similar function, called combine, has been already adopted by Hadoop, it operates immediately after a map task solely for its generated data, failing to exploit the data aggregation opportunities among multiple tasks on different machines. We jointly consider data partition and aggregation for a MapReduce job with an objective that is to minimize the total network traffic. In particular, we propose a distributed algorithm for big data applications by decomposing the original large-scale problem into several subproblems that can be solved in parallel. Moreover, an online algorithm is designed to deal with the data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost in both offline and online cases.

INTRODUCTION

MapReduce has emerged as the most popular computing framework for big data processing due to its simple programming model and automatic management of parallel execution. MapReduce and its open source implementation Hadoop have been adopted by leading companies, such as Yahoo!, Google and Facebook, for various big data applications, such as machine learning bioinformatics and cybersecurity. MapReduce divides a computation into two main phases, namely map and reduce which in turn are carried out by several map tasks and reduce tasks, respectively. In the map phase, map tasks are launched in parallel to convert the original input splits into intermediate data in a form of key/value pairs. These key/value pairs are stored on local machine and organized into multiple data partitions, one per reduce task. In the reduce phase, each reduce task fetches its own share of data partitions from all map tasks to generate the final result.

There is a shuffle step between map and reduce phase.

In this step, the data produced by the map phase are ordered, partitioned and transferred to the appropriate machines executing the reduce phase. The resulting network traffic pattern from all map tasks to all reduce tasks can cause a great volume of network traffic, imposing a serious constraint on the efficiency of data analytic applications. For example, with tens of thousands of machines, data shuffling accounts for 58.6% of the cross-pod traffic and amounts to over 200 petabytes in total in the analysis of SCOPE jobs. For shuffle-heavy MapReduce tasks, the high traffic could incur considerable performance overhead up to 30-40 % as shown in default, intermediate data are shuffled according to a hash function in Hadoop, which would lead to large network traffic because it ignores network topology and data size associated with each key.

We consider a toy example with two map tasks and two reduce tasks, where intermediate data of three keys K1, K2, and K3 are denoted by rectangle bars under each machine. If the hash function assigns data of K1 and K3 to reducer 1, and K2 to reducer 2, a large amount of traffic will go through the top switch. To tackle this problem incurred by the traffic-oblivious partition scheme, we take into account of both task locations and data size associated with each key in this paper. By assigning keys with larger data size to reduce tasks closer to map tasks, network traffic can be significantly reduced. In the same example above, if we assign K1 and K3 to reducer 2, and K2 to reducer 1, as shown in Fig. 1(b), the data transferred through the top switch will be significantly reduced.

To further reduce network traffic within a MapReduce job, we consider to aggregate data with the same keys before sending them to remote reduce tasks. Although a similar function, called combine, has been already adopted by Hadoop, it operates immediately after a map task solely for its generated data, failing to exploit the data aggregation opportunities among multiple tasks on different machines. As an example shown in Fig. 2(a), in the traditional scheme, two map tasks individually send data of key K1 to the reduce task. If we aggregate the data of the same keys before sending them over the top switch, as shown in Fig. 2(b), the network traffic will be reduced.

In this paper, we jointly consider data partition and aggregation for a MapReduce job with an objective that is to minimize the total network traffic. In particular, we propose a distributed algorithm for big data applications by decomposing the original large-scale problem into several subproblems that can be solved in parallel. Moreover, an online algorithm is designed to deal with the data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost in both offline and online cases.

LITRATURE SURVEY

MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS

AUTHOR: Dean and S. Ghemawat

PUBLISH: Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008.

EXPLANATION:

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google’s clusters every day.

CLOUDBLAST: COMBINING MAPREDUCE AND VIRTUALIZATION ON DISTRIBUTED RESOURCES FOR BIOINFORMATICS APPLICATIONS

AUTHOR: A. Matsunaga, M. Tsugawa, and J. Fortes,

PUBLISH: IEEE Fourth International Conference on. IEEE, 2008, pp. 222–229.

EXPLANATION:

This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution environments and commonly used data sets into flexibly deployable virtual machines, and network virtualization to connect resources behind firewalls/NATs while preserving the necessary performance and the communication environment. An implementation of this approach is described and used to demonstrate and evaluate the proposed approach. The implementation integrates Hadoop, Virtual Workspaces, and ViNe as the MapReduce, virtual machine and virtual network technologies, respectively, to deploy the commonly used bioinformatics tool NCBI BLAST on a WAN-based test bed consisting of clusters at two distinct locations, the University of Florida and the University of Chicago. This WAN-based implementation, called CloudBLAST, was evaluated against both non-virtualized and LAN-based implementations in order to assess the overheads of machine and network virtualization, which were shown to be insignificant. To compare the proposed approach against an MPI-based solution, CloudBLAST performance was experimentally contrasted against the publicly available mpiBLAST on the same WAN-based test bed. Both versions demonstrated performance gains as the number of available processors increased, with CloudBLAST delivering speedups of 57 against 52.4 of MPI version, when 64 processors on 2 sites were used. The results encourage the use of the proposed approach for the execution of large-scale bioinformatics applications on emerging distributed environments that provide access to computing resources as a service.

MAP TASK SCHEDULING IN MAPREDUCE WITH DATA LOCALITY: THROUGHPUT AND HEAVY-TRAFFIC OPTIMALITY

AUTHOR: W. Wang, K. Zhu, L. Ying, J. Tan, and L. Zhang

PUBLISH: INFOCOM, 2013 Proceedings IEEE. IEEE, 2013, pp. 1609–1617.

EXPLANATION:

Scheduling map tasks to improve data locality is crucial to the performance of MapReduce. Many works have been devoted to increasing data locality for better efficiency. However, to the best of our knowledge, fundamental limits of MapReduce computing clusters with data locality, including the capacity region and theoretical bounds on the delay performance, have not been studied. In this paper, we address these problems from a stochastic network perspective. Our focus is to strike the right balance between data-locality and load-balancing to simultaneously maximize throughput and minimize delay.

We present a new queueing architecture and propose a map task scheduling algorithm constituted by the Join the Shortest Queue policy together with the MaxWeight policy. We identify an outer bound on the capacity region, and then prove that the proposed algorithm stabilizes any arrival rate vector strictly within this outer bound. It shows that the algorithm is throughput optimal and the outer bound coincides with the actual capacity region. Further, we study the number of backlogged tasks under the proposed algorithm, which is directly related to the delay performance based on Little’s law. We prove that the proposed algorithm is heavy-traffic optimal, i.e., it asymptotically minimizes the number of backlogged tasks as the arrival rate vector approaches the boundary of the capacity region. Therefore, the proposed algorithm is also delay optimal in the heavy-traffic regime.

SYSTEM ANALYSIS

EXISTING SYSTEM:

Existing problem of optimizing network usage in MapReduce scheduling in the reason that we are interested in network usage is twofold. Firstly, network utilization is a quantity of independent interest, as it is directly related to the throughput of the system. Note that the total amount of data processed in unit time is simply (CPU utilization)·(CPU capacity)+ (network utilization)·(network capacity). CPU utilization will always be 1 as long as there are enough jobs in the map queue, but network utilization can be very sensitive to scheduling network utilization has been identified as a key component in optimization of MapReduce systems in several previous works.

Network usage could lead us to algorithms with smaller mean response time. We find the main motivation for this direction of our work in the results of the aforementioned overlap between map and shuffle phases, are shown to yield significantly better mean response time than Hadoop’s fair scheduler. However, we observed that neither of these two algorithms explicitly attempted to optimize network usage, which suggested room for improvement. MapReduce has become one of the most popular frameworks for large-scale distributed computing, there exists a huge body of work regarding performance optimization of MapReduce.

For instance, researchers have tried to optimize MapReduce systems by efficiently detecting and eliminating the so-called “stragglers” providing better locality of data preventing starvation caused by large jobs analyzing the problem from a purely theoretical viewpoint of shuffle workload available at any given time is closely related to the output rate of the map phase, due to the inherent dependency between the map and shuffle phases. In particular, when the job that is being processed is ‘map-heavy,’ the available workload of the same job in the shuffle phase is upper-bounded by the output rate of the map phase. Therefore, poor scheduling of map tasks can have adverse effects on the throughput of the shuffle phase, causing the network to be idle and the efficiency of the entire system to decrease.

DISADVANTAGES:

Existing model, called the overlapping tandem queue model, is a job-level model for MapReduce where the map and shuffle phases of the MapReduce framework are modeled as two queues that are put in tandem. Since it is a job-level model, each job is represented by only the map size and the shuffle size simplification is justified by the introduction of two main assumptions. The first assumption states that each job consists of a large number of small-sized tasks, which allows us to represent the progress of each phase by real numbers.

The job-level model offers two big disadvantages over the more complicated task-level models.

Firstly, it gives rise to algorithms that are much simpler than those of task-level models, which enhances chances of being deployed in an actual system.

Secondly, the number of jobs in a system is often smaller than the number of tasks by several orders of magnitude, making the problem computationally much less strenuous note that there are still some questions to be studied regarding the general applicability of the additional assumptions of the job-level model, which are interesting research questions in their own light

PROPOSED SYSTEM:

In this paper, we jointly consider data partition and aggregation for a MapReduce job with an objective that is to minimize the total network traffic. In particular, we propose a distributed algorithm for big data applications by decomposing the original large-scale problem into several subproblems that can be solved in parallel. Moreover, an online algorithm is designed to deal with the data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost in both offline and online cases.

MapReduce resource allocation system, to enhance the performance of MapReduce jobs in the cloud by locating intermediate data to the local machines or close-by physical machines in this locality-awareness reduces network traffic in the shuffle phase generated in the cloud data center. However, little work has studied to optimize network performance of the shuffle process that generates large amounts of data traffic in MapReduce jobs. A critical factor to the network performance in the shuffle phase is the intermediate data partition. The default scheme adopted by Hadoop is hash-based partition that would yield unbalanced loads among reduce tasks due to its unawareness of the data size associated with each key.

We have developed a fairness-aware key partition approach that keeps track of the distribution of intermediate keys’ frequencies, and guarantees a fair distribution among reduce tasks. have introduced a combiner function that reduces the amount of data to be shuffled and merged to reduce tasks an in-mapper combining scheme by exploiting the fact that mappers can preserve state across the processing of multiple input key/value pairs and defer emission of intermediate data until all input records have been processed. Both proposals are constrained to a single map task, ignoring the data aggregation opportunities from multiple map tasks a MapReduce-like system to decrease the traffic by pushing aggregation from the edge into the network.

ADVANTAGES:

• Our proposed distributed algorithm and the optimal solution obtained by solving the MILP formulation. Due to the high computational complexity of the MILP formulation, we consider small-scale problem instances with 10 keys in this set of simulations.
• Our distributed algorithm is very close to the optimal solution. Although network traffic cost increases as the number of keys grows for all algorithms, the performance enhancement of our proposed algorithms to the other two schemes becomes larger.
• Our distributed algorithm with the other two schemes a default simulation setting with a number of parameters, and then study the performance by changing one parameter while fixing others. We consider a MapReduce job with 100 keys and other parameters are the same above. the network traffic cost shows as an increasing function of number of keys from 1 to 100 under all algorithms.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Script :           Java Script
• Tool :           Netbean 7
• Document :           MS-Office 2007

Abstract

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users’ forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users’ forum posts. We then introduced a novel network-based approach for modeling users’ forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments. Index Terms—Data mining, complex networks, neural networks, semantic web, social computing.

INTRODUCTION

Social media is providing limitless opportunities for patients to discuss their experiences with drugs and devices, and for companies to receive feedback on their products and services [1]–[3]. Pharmaceutical companies are prioritizing social network monitoring within their IT departments, creating an opportunity for rapid dissemination and feedback of products and services to optimize and enhance delivery, increase turnover and profit, and reduce costs [4]. Social media data harvesting for bio-surveillance have also been reported [5]. Social media enables a virtual networking environment. Modeling social media using available network modeling and computational tools is one way of extracting knowledge and trends from the information ‘cloud:’ a social network is a structure made of nodes and edges that connect nodes in various relationships. Graphical representation is the most common method to visually represent the information. Network modeling could also be used for studying the simulation of network properties and its internal dynamics.

A sociomatrix can be used to construct representations of a social network structure. Node degree, network density, and other large-scale parameters can derive information about the importance of certain entities within the network. Such communities are clusters or modules. Specific algorithms can perform network-clustering, one of the fundamental tasks in network analysis. Detecting particular user communities requires identifying specific, networked nodes that will allow information extraction. Healthcare providers could use patient opinion to improve their services. Physicians could collect feedback from other doctors and patients to improve their treatment recommendations and results. Patients could use other consumers’ knowledge in making better-informed healthcare decisions.

The nature of social networks makes data collection difficult. Several methods have been employed, such as link mining [6], classification through links [7], predictions based on objects [8], links [9], existence [10], estimation [11], object [12], group [13], and subgroup detection [14], and mining the data [15], [16]. Link prediction, viral marketing, online discussion groups (and rankings) allow for the development of solutions based on user feedback.

Traditional social sciences use surveys and involve subjects in the data collection process, resulting in small sample sizes per study. With social media, more content is readily available, particularly when combined with web-crawling and scraping software that would allow real-time monitoring of changes within the network. Previous studies used technical solutions to extract user sentiment on influenza [17], technology stocks [18], context and sentence structure [19], online shopping [20], multiple classifications [21], government health monitoring [22], specific terms relating to consumer satisfaction [23], polarity of newspaper articles [24], and assessment of user satisfaction from companies [25], [26]. Despite the extensive literature, none have identified influential users, and how forum relationships affect network dynamics. In the first stage of our current study, we employ exploratory analysis using the self-organizing maps (SOMs) to assess correlations between user posts and positive or negative opinion on the drug. In the second stage, we model the users and their posts using a network-based approach.

We build on our previous study [27] and use an enhanced method for identifying user communities (modules) and influential users therein. The current approach effectively searches for potential levels of organization (scales) within the networks and uncovers dense modules using a partition stability quality measure [28]. The approach enables us to find the optimal network partition. We subsequently enrich the retrieved modules with word frequency information from module-contained users posts to derive local and global measures of users opinion and raise flag on potential side effects of Erlotinib, a drug used in the treatment of one of the most prevalent cancers: lung cancer [29].

ABSTRACT:

In this paper, we present a distributed structured approach to Sybil attack. This is derived from the fact that our approach is based on the neighbor similarity trust relationship among the neighbor peers. Given a P2P e-commerce trust relationship based on interest, the transactions among peers are flexible as each peer can decide to trade with another peer any time. A peer doesn’t have to consult others in a group unless a recommendation is needed. This approach shows the advantage in exploiting the similarity trust relationship among peers in which the peers are able to monitor each other.

Our contribution in this paper is threefold:

1) We propose SybilTrust that can identify and protect honest peers from Sybil attack. The Sybil peers can have their trust canceled and dismissed from a group.

2) Based on the group infrastructure in P2P e-commerce, each neighbor is connected to the peers by the success of the transactions it makes or the trust evaluation level. A peer can only be recognized as a neighbor depending on whether or not trust level is sustained over a threshold value.

3) SybilTrust enables neighbor peers to carry recommendation identifiers among the peers in a group. This ensures that the group detection algorithms to identify Sybil attack peers to be efficient and scalable in large P2P e-commerce networks.

GOAL OF THE PROJECT:

The goal of trust systems is to ensure that honest peers are accurately identified as trustworthy and Sybil peers as untrustworthy. To unify terminology, we call all identities created by malicious users as Sybil peers. In a P2P e-commerce application scenario, most of the trust considerations depend on the historical factors of the peers. The influence of Sybil identities can be reduced based on the historical behavior and recommendations from other peers. For example, a peer can give positive a recommendation to a peer which is discovered is a Sybil or malicious peer. This can diminish the influence of Sybil identities hence reduce Sybil attack. A peer which has been giving dishonest recommendations will have its trust level reduced. In case it reaches a certain threshold level, the peer can be expelled from the group. Each peer has an identity, which is either honest or Sybil.

A Sybil identity can be an identity owned by a malicious user, or it can be a bribed/stolen identity, or it can be a fake identity obtained through a Sybil attack. These Sybil attack peers are employed to target honest peers and hence subvert the system. In Sybil attack, a single malicious user creates a large number of peer identities called sybils. These sybils are used to launch security attacks, both at the application level and at the overlay level, application level, sybils can target other honest peers while transacting with them, whereas at the overlay level, sybils can disrupt the services offered by the overlay layer like routing, data storage, lookup, etc. In trust systems, colluding Sybil peers may artificially increase a (malicious) peer’s rating (e.g., eBay).

INTRODUCTION:

P2P networks range from communication systems like email and instant messaging to collaborative content rating, recommendation, and delivery systems such as YouTube, Gnutela, Facebook, Digg, and BitTorrent. They allow any user to join the system easily at the expense of trust, with very little validation control. P2P overlay networks are known for their many desired attributes like openness, anonymity, decentralized nature, self-organization, scalability, and fault tolerance. Each peer plays the dual role of client as well as server, meaning that each has its own control. All the resources utilized in the P2P infrastructure are contributed by the peers themselves unlike traditional methods where a central authority control is used. Peers can collude and do all sorts of malicious activities in the open-access distributed systems. These malicious behaviors lead to service quality degradation and monetary loss among business partners. Peers are vulnerable to exploitation, due to the open and near-zero cost of creating new identities. The peer identities are then utilized to influence the behavior of the system.

However, if a single defective entity can present multiple identities, it can control a substantial fraction of the system, thereby undermining the redundancy. The number of identities that an attacker can generate depends on the attacker’s resources such as bandwidth, memory, and computational power. The goal of trust systems is to ensure that honest peers are accurately identified as trustworthy and Sybil peers as untrustworthy. To unify terminology, we call all identities created by malicious users as Sybil peers. In a P2P e-commerce application scenario, most of the trust considerations depend on the historical factors of the peers. The influence of Sybil identities can be reduced based on the historical behavior and recommendations from other peers. For example, a peer can give positive a recommendation to a peer which is discovered is a Sybil or malicious peer. This can diminish the influence of Sybil identities hence reduce Sybil attack. A peer which has been giving dishonest recommendations will have its trust level reduced. In case it reaches a certain threshold level, the peer can be expelled from the group.

Each peer has an identity, which is either honest or Sybil. A Sybil identity can be an identity owned by a malicious user, or it can be a bribed/stolen identity, or it can be a fake identity obtained through a Sybil attack. These Sybil attack peers are employed to target honest peers and hence subvert the system. In Sybil attack, a single malicious user creates a large number of peer identities called sybils. These sybils are used to launch security attacks, both at the application level and at the overlay level at the application level, sybils can target other honest peers while transacting with them, whereas at the overlay level, sybils can disrupt the services offered by the overlay layer like routing, data storage, lookup, etc. In trust systems, colluding Sybil peers may artificially increase a (malicious) peer’s rating (e.g., eBay). Systems like Credence rely on a trusted central authority to prevent maliciousness.

Defending against Sybil attack is quite a challenging task. A peer can pretend to be trusted with a hidden motive. The peer can pollute the system with bogus information, which interferes with genuine business transactions and functioning of the systems. This must be counter prevented to protect the honest peers. The link between an honest peer and a Sybil peer is known as an attack edge. As each edge involved resembles a human-established trust, it is difficult for the adversary to introduce an excessive number of attack edges. The only known promising defense against Sybil attack is to use social networks to perform user admission control and limit the number of bogus identities admitted to a system. The use of social networks between two peers represents real-world trust relationship between users. In addition, authentication-based mechanisms are used to verify the identities of the peers using shared encryption keys, or location information.

LITRATURE SURVEY:

KEEP YOUR FRIENDS CLOSE: INCORPORATING TRUST INTO SOCIAL NETWORK-BASED SYBIL DEFENSES

AUTHOR: A. Mohaisen, N. Hopper, and Y. Kim

PUBLISH: Proc. IEEE Int. Conf. Comput. Commun., 2011, pp. 1–9.

EXPLANATION:

Social network-based Sybil defenses exploit the algorithmic properties of social graphs to infer the extent to which an arbitrary node in such a graph should be trusted. However, these systems do not consider the different amounts of trust represented by different graphs, and different levels of trust between nodes, though trust is being a crucial requirement in these systems. For instance, co-authors in an academic collaboration graph are trusted in a different manner than social friends. Furthermore, some social friends are more trusted than others. However, previous designs for social network-based Sybil defenses have not considered the inherent trust properties of the graphs they use. In this paper we introduce several designs to tune the performance of Sybil defenses by accounting for differential trust in social graphs and modeling these trust values by biasing random walks performed on these graphs. Surprisingly, we find that the cost function, the required length of random walks to accept all honest nodes with overwhelming probability, is much greater in graphs with high trust values, such as co-author graphs, than in graphs with low trust values such as online social networks. We show that this behavior is due to the community structure in high-trust graphs, requiring longer walk to traverse multiple communities. Furthermore, we show that our proposed designs to account for trust, while increase the cost function of graphs with low trust value, decrease the advantage of attacker.

FOOTPRINT: DETECTING SYBIL ATTACKS IN URBAN VEHICULAR NETWORKS

AUTHOR: S. Chang, Y. Qi, H. Zhu, J. Zhao, and X. Shen

PUBLISH: IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 6, pp. 1103–1114, Jun. 2012.

EXPLANATION:

In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily launch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, Footprint, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this RSU. We design a location-hidden authorized message generation scheme for two objectives: first, RSU signatures on messages are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second, two authorized messages signed by the same RSU within the same given period of time (temporarily linkable) are recognizable so that they can be used for identification. With the temporal limitation on the linkability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a location-hidden trajectory for location-privacy-preserved identification by collecting a consecutive series of authorized messages. Utilizing social relationship among trajectories according to the similarity definition of two trajectories, Footprint can recognize and therefore dismiss “communities” of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of Footprint.

SYBILLIMIT: A NEAROPTIMAL SOCIAL NETWORK DEFENSE AGAINST SYBIL ATTACK

AUTHOR: H. Yu, P. Gibbons, M. Kaminsky, and F. Xiao

PUBLISH: IEEE/ACM Trans. Netw., vol. 18, no. 3, pp. 3–17, Jun. 2010.

EXPLANATION:

Decentralized distributed systems such as peer-to-peer systems are particularly vulnerable to sybil attacks, where a malicious user pretends to have multiple identities (called sybil nodes). Without a trusted central authority, defending against sybil attacks is quite challenging. Among the small number of decentralized approaches, our recent SybilGuard protocol [H. Yu et al., 2006] leverages a key insight on social networks to bound the number of sybil nodes accepted. Although its direction is promising, SybilGuard can allow a large number of sybil nodes to be accepted. Furthermore, SybilGuard assumes that social networks are fast mixing, which has never been confirmed in the real world. This paper presents the novel SybilLimit protocol that leverages the same insight as SybilGuard but offers dramatically improved and near-optimal guarantees. The number of sybil nodes accepted is reduced by a factor of ominus(radicn), or around 200 times in our experiments for a million-node system. We further prove that SybilLimit’s guarantee is at most a log n factor away from optimal, when considering approaches based on fast-mixing social networks. Finally, based on three large-scale real-world social networks, we provide the first evidence that real-world social networks are indeed fast mixing. This validates the fundamental assumption behind SybilLimit’s and SybilGuard’s approach.

SYSTEM ANALYSIS

EXISTING SYSTEM:

Existing work on Sybil attack makes use of social networks to eliminate Sybil attack, and the findings are based on preventing Sybil identities. In this paper, we propose the use of neighbor similarity trust in a group P2P ecommerce based on interest relationships, to eliminate maliciousness among the peers. This is referred to as SybilTrust. In SybilTrust, the interest based group infrastructure peers have a neighbor similarity trust between each other, hence they are able to prevent Sybil attack. SybilTrust gives a better relationship in e-commerce transactions as the peers create a link between peer neighbors. This provides an important avenue for peers to advertise their products to other interested peers and to know new market destinations and contacts as well. In addition, the group enables a peer to join P2P e-commerce network and makes identity more difficult.

Peers use self-certifying identifiers that are exchanged when they initially come into contact. These can be used as public keys to verify digital signatures on the messages sent by their neighbors. We note that, all communications between peers are digitally signed. In this kind of relationship, we use neighbors as our point of reference to address Sybil attack. In a group, whatever admission we set, there are honest, malicious, and Sybil peers who are authenticated by an admission control mechanism to join the group. More honest peers are admitted compared to malicious peers, where the trust association is aimed at positive results. The knowledge of the graph may reside in a single party, or be distributed across all users.

DISADVANTAGES:

Sybil peer trades with very few unsuccessful transactions, we can deduce the peer is a Sybil peer. This is supported by our approach which proposes peers existing in a group have six types of keys.

The keys which exist mostly are pairwise keys supported by the group keys. We also note if an honest group has a link with another group which has Sybil peers, the Sybil group tend to have information which is not complete.

1. Fake Users Enters Easy.
2. This makes Sybil attacks.

PROPOSED SYSTEM:

In this paper, we assume there are three kinds of peers in the system: legitimate peers, malicious peers, and Sybil peers. Each malicious peer cheats its neighbors by creating multiple identity, referred to as Sybil peers. In this paper, P2P e-commerce communities are in several groups. A group can be either open or restrictive depending on the interest of the peers. We investigate the peers belonging to a certain interest group. In each group, there is a group leader who is responsible for managing coordination of activities in a group.

The principal building block of Sybil Trust approach is the identifier distribution process. In the approach, all the peers with similar behavior in a group can be used as identifier source. They can send identifiers to others as the system regulates. If a peer sends less or more, the system can be having a Sybil attack peer. The information can be broadcast to the rest of the peers in a group. When peers join a group, they acquire different identities in reference to the group. Each peer has neighbors in the group and outside the group. Sybil attack peers forged by the same malicious peer have the same set of physical neighbors that a malicious peer has.

Each neighbor is connected to the peers by the success of the transaction it makes or the trust evaluation level. To detect the Sybil attack, where a peer can have different identity, a peer is evaluated in reference to its trustworthiness and the similarity to the neighbors. If the neighbors do not have same trust data as the concerned peer, including its position, it can be detected that the peer has multiple identity and is cheating

ADVANTAGES:

Our perception is that, the attacker controls a number of neighbor similarity peers, whereby a randomly chosen identifier source is relatively “far away” from most Sybil attack peer relationship. Every peer uses a “reversed” routing table. The source peer will always send some information to the peers which have neighbor similarity trust. However, if they do not reply, it can black list them. If they do reply and the source is overwhelmed by the overhead of such replies, then the adversary is effectively launching a DoS attack. Notice that the adversary can launch a DoS attack against the source. This enables two peers to propagate their public keys and IP addresses backward along the route to learn about the peers.

• It is Helpful to find Sybil Attacks.
• It is used to Find Fake UserID.
• It is feasible to limit the number of attack edges in online social networks by relationship rating.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Script :           Java Script
• Tools :           Netbeans 7
• Document :           MS-Office 2007

ABSTRACT:

File sharing applications in mobile ad hoc networks (MANETs) have attracted more and more attention in recent years. The efficiency of file querying suffers from the distinctive properties of such networks including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica creation with minimum average querying delay.

Specifically, current file replication protocols in mobile ad hoc networks have two shortcomings. First, they lack a rule to allocate limited resources to different files in order to minimize the average querying delay. Second, they simply consider storage as available resources for replicas, but neglect the fact that the file holders’ frequency of meeting other nodes also plays an important role in determining file availability. Actually, a node that has a higher meeting frequency with others provides higher availability to its files. This becomes even more evident in sparsely distributed MANETs, in which nodes meet disruptively.

In this paper, we introduce a new concept of resource for file replication, which considers both node storage and node meeting ability. We theoretically study the influence of resource allocation on the average querying delay and derive an optimal file replication rule (OFRR) that allocates resources to each file based on its popularity and size. We then propose a file replication protocol based on the rule, which approximates the minimum global querying delay in a fully distributed manner. Our experiment and simulation results show the superior performance of the proposed protocol in comparison with other representative replication protocols.

INTRODUCTION

With the increasing popularity of mobile devices, e.g., smartphones and laptops, we envision the future of MANETs consisted of these mobile devices. By MANETs, we refer to both normal MANETs and disconnected MANETs, also known as delay tolerant networks (DTNs). The former has a relatively dense node distribution in an area while the latter has sparsely distributed nodes that meet each other opportunistically. On the other side, the emerging of mobile file sharing applications on the peer-to-peer (P2P) file sharing over such MANETs. The local P2P file sharing model provides three advantages. First, it enables file sharing when no base stations are available (e.g., in rural areas). Second, with the P2P architecture, the bottleneck on overloaded servers in current clientserver based file sharing systems can be avoided. Third, it exploits otherwise wasted peer to peer communication opportunities among mobile nodes. As a result, nodes can freely and unobtrusively access and share files in the distributed MANET environment, which can possibly support interesting applications.

For example, mobile nodes can share files based on users’ proximity in the same building or in a local community. Tourists can share their travel experiences or emergency information with other tourists through digital devices directly even when no base station is available in remote areas. Drivers can share road information through the vehicle-to-vehicle communication. However, the distinctive properties of MANETs, i.e., node mobility, limited communication range and resource, have rendered many difficulties in realizing such a P2P file sharing system. For example, file searching turns out to be difficult since nodes in MANETs move around freely and can exchange information only when they are within the communication range. Broadcasting can quickly discover files, but it leads to the broadcast storm problem with high energy consumption.

Probabilistic routing and file discovery protocols avoid broadcasting by forwarding a query to a node with higher probability of meeting the destination. But the opportunistic encountering of nodes in MANETs makes file searching and retrieval non-deterministic. File replication is an effective way to enhance file availability and reduce file querying delay. It creates replicas for a file to improve its probability of being encountered by requests. Unfortunately, it is impractical and inefficient to enable every node to hold the replicas of all files in the system considering limited node resources. Also, file querying delay is always a main concern in a file sharing system. Users often desire to receive their requested files quickly no matter whether the files are popular or not. Thus, a critical issue is raised for further investigation: how to allocate the limited resource in the network to different files for replication so that the overall average file querying delay is minimized? Recently, a number of file replication protocols have been proposed for MANETs. In these protocols, each individual node replicates files it frequently queries or a group of nodes create one replica for each file they frequently query. In the former, redundant replicas are easily created in the system, thereby wasting resources.

In the latter, though redundant replicas are reduced by group based cooperation, neighboring nodes may separate from each other due to node mobility, leading to large query delay. There are also some works addressing content caching in disconnected MANETs/ DTNs for efficient data retrieval or message routing. They basically cache data that are frequently queried on places that are visited frequently by mobile nodes. Both the two categories of replication methods fail to thoroughly consider that a node’s mobility affects the availability of its files. In spite of efforts, current file replication protocols lack a rule to allocate limited resources to files for replica creation in order to achieve the minimum average querying delay, i.e., global search efficiency optimization under limited resources. They simply consider storage as the resource for replicas, but neglect that a node’s frequency to meet other nodes (meeting ability in short) also influences the availability of its files. Files in a node with a higher meeting ability have higher availability.

LITRATURE SURVEY

CONTACT DURATION AWARE DATA REPLICATION IN DELAY TOLERANT NETWORKS

AUTHOR: X. Zhuo, Q. Li, W. Gao, G. Cao, and Y. Dai

PUBLISH: Proc. IEEE 19th Int’l Conf. Network Protocols (ICNP), 2011.

EXPLANATION:

The recent popularization of hand-held mobile devices, such as smartphones, enables the inter-connectivity among mobile users without the support of Internet infrastructure. When mobile users move and contact each other opportunistically, they form a Delay Tolerant Network (DTN), which can be exploited to share data among them. Data replication is one of the common techniques for such data sharing. However, the unstable network topology and limited contact duration in DTNs make it difficult to directly apply traditional data replication schemes. Although there are a few existing studies on data replication in DTNs, they generally ignore the contact duration limits. In this paper, we recognize the deficiency of existing data replication schemes which treat the complete data item as the replication unit, and propose to replicate data at the packet level. We analytically formulate the contact duration aware data replication problem and give a centralized solution to better utilize the limited storage buffers and the contact opportunities. We further propose a practical contact Duration Aware Replication Algorithm (DARA) which operates in a fully distributed manner and reduces the computational complexity. Extensive simulations on both synthetic and realistic traces show that our distributed scheme achieves close-to-optimal performance, and outperforms other existing replication schemes.

SOCIAL-BASED COOPERATIVE CACHING IN DTNS: A CONTACT DURATION AWARE APPROACH

AUTHOR: X. Zhuo, Q. Li, G. Cao, Y. Dai, B.K. Szymanski, and T.L. Porta,

PUBLISH: Proc. IEEE Eighth Int’l Conf. Mobile Adhoc and Sensor Systems (MASS), 2011.

EXPLANATION:

Data access is an important issue in Delay Tolerant Networks (DTNs), and a common technique to improve the performance of data access is cooperative caching. However, due to the unpredictable node mobility in DTNs, traditional caching schemes cannot be directly applied. In this paper, we propose DAC, a novel caching protocol adaptive to the challenging environment of DTNs. Specifically, we exploit the social community structure to combat the unstable network topology in DTNs. We propose a new centrality metric to evaluate the caching capability of each node within a community, and solutions based on this metric are proposed to determine where to cache. More importantly, we consider the impact of the contact duration limitation on cooperative caching, which has been ignored by the existing works. We prove that the marginal caching benefit that a node can provide diminishes when more data is cached. We derive an adaptive caching bound for each mobile node according to its specific contact patterns with others, to limit the amount of data it caches. In this way, both the storage space and the contact opportunities are better utilized. To mitigate the coupon collector’s problem, network coding techniques are used to further improve the caching efficiency. Extensive trace-driven simulations show that our cooperative caching protocol can significantly improve the performance of data access in DTNs.

SEDUM: EXPLOITING SOCIAL NETWORKS IN UTILITY-BASED DISTRIBUTED ROUTING FOR DTNS

AUTHOR: Z. Li and H. Shen

PUBLISH: IEEE Trans. Computers, vol. 62, no. 1, pp. 83-97, Jan. 2012.

EXPLANATION:

However, current probabilistic forwarding methods only consider node contact frequency in calculating the utility while neglecting the influence of contact duration on the throughput, though both contact frequency and contact duration reflect the node movement pattern in a social network. In this paper, we theoretically prove that considering both factors leads to higher throughput than considering only contact frequency. To fully exploit a social network for high throughput and low routing delay, we propose a Social network oriented and duration utility-based distributed multicopy routing protocol (SEDUM) for DTNs. SEDUM is distinguished by three features. First, it considers both contact frequency and duration in node movement patterns of social networks. Second, it uses multicopy routing and can discover the minimum number of copies of a message to achieve a desired routing delay. Third, it has an effective buffer management mechanism to increase throughput and decrease routing delay. Theoretical analysis and simulation results show that SEDUM provides high throughput and low routing delay compared to existing routing approaches. The results conform to our expectation that considering both contact frequency and duration for delivery utility in routing can achieve higher throughput than considering only contact frequency, especially in a highly dynamic environment with large routing messages.

SYSTEM ANALYSIS

EXISTING SYSTEM:

This work focuses on Delay Tolerant Networks (DTNs) in a social network environment. DTNs do not have a complete path from a source to a destination most of the time. Previous data routing approaches in DTNs are primarily based on either flooding or single-copy routing. However, these methods incur either high overhead due to excessive transmissions or long delays due to suboptimal choices for relay nodes. Probabilistic forwarding that forwards a message to a node with a higher delivery utility enhances single-copy routing.

Previous file sharing applications in mobile ad hoc networks (MANETs) have attracted more efficiency of file querying suffers from the distinctive properties of MANETs including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica sharing with minimum average querying delay communication links between mobile nodes are transient and network maintenance overhead is a major performance bottleneck for data transmission. Low node density makes it difficult to establish end-to-end connection, thus impeding a continuous end-to-end path between a source and a destination.

DTN networks for communication in outer space, but is now directly accessible from our pockets both the characteristics of MANETs and the requirements of P2P file sharing an application layer overlay network. We port a DTN type solution into an infrastructure-less environment like MANETs and leverage peer mobility to reach data in other disconnected networks. This is done by implementing an asynchronous communication model, store-delegate-and-forward, like DTNs, where a peer can delegate unaccomplished file download or query tasks to special peers. To improve data transmission performance while reducing communication overhead, we select these special peers by the expectation of encountering them again in future and assign them different download starting point on the file.

DISADVANTAGES:

• Limited communication range and resource have rendered many difficulties in realizing such a P2P file sharing system. For example, file searching turns out to be difficult since nodes in MANETs move around freely and can exchange information only when they are within the communication range.

• The disadvantage is that it lacked of transparency. Receiving a URL explicitly points to certain data replica and that the browser will become aware of the switching between the different machines.
• And for scalability, the necessity of making contact with is always the same, the single service machine can make it bottleneck as the number of clients increase which makes situation worse.

PROPOSED SYSTEM:

We propose a distributed file replication protocol that can approximately realize the optimal file replication rule with the two mobility models in a distributed manner in the OFRR in the two mobility models (i.e., Equations (22) and (28)) have the same form, we present the protocol in this section without indicating the specific mobility model. We first introduce the challenges to realize the OFRR and our solutions. We then propose a replication protocol to realize OFRR and analyze the effect of the protocol.

We propose the priority competition and split file replication protocol (PCS). We first introduce how a node retrieves the parameters needed in PCS and then present the detail of PCS. we briefly prove the effectiveness of PCS. We refer to the process in which a node tries to copy a file to its neighbors as one round of replica distribution. Recall that when a replica is created for a file with P, the two copies will replicate files with priority P =2 in the next round. This means that the creation of replicas will not increase the overall P of the file. Also, after each round, the priority value of each file or replica is updated based on the received requests for the file.

Then, though some replicas may be deleted in the competition, the total amount of requests for the file remains stable, making the sum of the Ps of all replicas and the original file roughly equal to the overall priority value of the file. Then, we can regard the replicas of a file as an entity that competes for available resource in the system with accumulated priority P in each round. Therefore, in each round of replica distribution, based on our design of PCS, the overall probability of creating a replica for an original file

ADVANTAGES:

The community-based mobility model has been used in content dissemination or routing algorithms for disconnected MANETs/DTNs to depict node mobility. In this model, the entire test area is split into different sub-areas, denoted as caves. Each cave holds one community.

RWP model, we can assume that the inter-meeting time among nodes follows exponential distribution. Then, the probability of meeting a node is independent with the previous encountered node. Therefore, we define the meeting ability of a node as the average number of nodes it meets in a unit time and use it to investigate the optimal file replication.

PCS, we used two routing protocols in the experiments. We first used the Static Wait protocol in the GENI experiment, in which each query stays on the source node waiting for the destination. We then used a probabilistic routing protocol (PROPHET) in which a node routes requests to the neighbor with the highest meeting ability.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM                                    –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Tools :           Netbeans 7
• Script :           Java Script
• Document :           MS-Office 2007

Abstract—Media streaming applications have recently attracted a large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand. Media content providers are charged for the amount of resources allocated (reserved) in the cloud. Most of the existing cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, an open problem is to decide on both the right amount of resources reserved in the cloud, and their reservation time such that the financial cost on the media content provider is minimized. We propose a simple—easy to implement—algorithm for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud. Based on the prediction of demand for streaming capacity, our algorithm is carefully designed to reduce the risk of making wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

INTRODUCTION
Media streaming applications have recently attracted large number of users in the Internet. In 2010, the number of video streams served increased 38.8 percent to 24.92 billion as compared to 2009 [1]. This huge demand creates a burden on centralized data centers at media content providers such as Video-on-Demand (VoD) providers to sustain the required QoS guarantees [2]. The problem becomes more critical with the increasing demand for higher bit rates required for the growing number of higherdefinition video quality desired by consumers. In this paper, we explore new approaches that mitigate the cost of streaming distribution on media content providers using cloud computing.
A media content provider needs to equip its data-center with over-provisioned (excessive) amount of resources in order to meet the strict QoS requirements of streaming traffic. Since it is possible to anticipate the size of usage peaks for streaming capacity in a daily, weekly, monthly, and yearly basis, a media content provider can make long term investments in infrastructure (e.g., bandwidth and computing capacities) to target the expected usage peak. However, this causes economic inefficiency problems in view of flashcrowd events. Since data-centers of a media content provider are equipped with resources that target the peak expected demand, most servers in a typical data-center of a media content provider are only used at about 30 percent of their capacity [3]. Hence, a huge amount of capacity at the servers will be idle most of the time, which is highly wasteful and inefficient. Cloud computing creates the possibility for media content providers to convert the upfront infrastructure investment to operating expenses charged by cloud providers (e. g., Netflix moved its streaming servers to Amazon Web Services (AWS) [4], [5]). Instead of buying over-provisioned servers and building private data-centres, media content providers can use computing and bandwidth resources of cloud service providers. Hence, a media content provider can be viewed as a re-seller of cloud resources, where it pays the cloud service provider for the streaming resources (bandwidth) served from the cloud directly to clients of the media content provider. This paradigm reduces the expenses of media content providers in terms of purchase and maintenance of over-provisioned resources at their data-centres.
In the cloud, the amount of allocated resources can be changed adaptively at a fine granularity, which is commonly referred to as auto-scaling. The auto-scaling ability of the cloud enhances resource utilization by matching the supply with the demand. So far, CPU and memory are the common resources offered by the cloud providers (e.g., Amazon EC2 [6]). However, recently, streaming resources (bandwidth) have become a feature offered by many cloud providers to users with intensive bandwidth demand (e.g.,
Amazon CloudFront and Octoshape) [5], [7], [8], [9].

The delay sensitive nature of media streaming traffic poses unique challenges due to the need for guaranteed throughput (i.e., download rate no smaller than the video playback rate) in order to enable users to smoothly watch video content on-line. Hence, the media content provider needs to allocate streaming resources in the cloud such that the demand for streaming capacity can be sustained at any instant of time.
The common type of resource provisioning plan that is offered by cloud providers is referred to as on-demand plan. This plan allows the media content provider to purchase resources upon needed. The pricing model that cloud providers employ for the on-demand plan is the pay-peruse. Another type of streaming resource provisioning plans that is offered by many cloud providers is based on resource reservation. With the reservation plan, the media content provider allocates (reserves) resources in advance and pricing is charged before the resources are utilized (upon receiving the request by the cloud provider, i.e., prepaid resources). The reserved streaming resources are basically the bandwidth (streaming data-rate) at which the cloud provider guarantees to deliver to clients of the media content provider (content viewers) according to the required QoS.
In general, the prices (tariffs) of the reservation plan are cheaper than those of the on-demand plan (i.e., time discount rates are only offered to the reserved (prepaid) resources). We consider a pricing model for resource reservation in the cloud that is based on non-linear time-discount tariffs. In such a pricing scheme, the cloud service provider offers higher discount rates to the resources reserved in the cloud for longer times. Such a pricing scheme enables a cloud service provider to better utilize its abundantly available resources because it encourages consumers to reserve resources in the cloud for longer times.
This pricing scheme is currently being used by many cloud providers [10]. See for example the pricing of virtual machines (VM) in the reservation phase defined by Amazon EC2 in February 2010. In this case, an open problem is to decide on both the optimum amount of resources reserved in the cloud (i.e., the prepaid allocated resources), and the optimum period of time during which those resources are reserved such that the monetary cost on the media content provider is minimized. In order for a media content provider to address this problem, prediction of future demand for streaming capacity is required to help with the resource reservation planning. Many methods have been proposed in prior works to predict the demand for streaming capacity [11], [12], [13], [14].
Our main contribution in this paper is a practical—easy to implement—Prediction-Based Resource Allocation algorithm (PBRA) that minimizes the monetary cost of resource reservation in the cloud by maximally exploiting discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud with some level of confidence in probabilistic sense. We first describe the system model. We formulate the problem based on the prediction of future demand for streaming capacity (Section 3). We then describe the design of our proposed algorithm for solving the problem (Section 4).
The results of our numerical evaluations and simulations show that the proposed algorithms significantly reduce the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

ABSTRACT:

We present a new method to identify navigation related Web usability problems based on comparing actual and anticipated usage patterns. The actual usage patterns can be extracted from Web server logs routinely recorded for operational websites by first processing the log data to identify users, user sessions, and user task-oriented transactions, and then applying a usage mining algorithm to discover patterns among actual usage paths. The anticipated usage, including information about both the path and time required for user-oriented tasks, is captured by our ideal user interactive path models constructed by cognitive experts based on their cognition of user behavior.

The comparison is performed via the mechanism of test MY SQL for checking results and identifying user navigation difficulties. The deviation data produced from this comparison can help us discover usability issues and suggest corrective actions to improve usability. A software tool was developed to automate a significant part of the activities involved. With an experiment on a small service-oriented website, we identified usability problems, which were cross-validated by domain experts, and quantified usability improvement by the higher task success rate and lower time and effort for given tasks after suggested corrections were implemented. This case study provides an initial validation of the applicability and effectiveness of our method.

INTRODUCTION

As the World Wide Web becomes prevalent today, building and ensuring easy-to-use Web systems is becoming a core competency for business survival. Usability is defined as the effectiveness, efficiency, and satisfaction with which specific users can complete specific tasks in a particular environment. Three basic Web design principles, i.e., structural firmness, functional convenience, and presentational delight, were identified to help improve users’ online experience. Structural firmness relates primarily to the characteristics that influence the website security and performance. Functional convenience refers to the availability of convenient characteristics, such as a site’s ease of use and ease of navigation, that help users’ interaction with the interface. Presentational delight refers to the website characteristics that stimulate users’ senses. Usability engineering provides methods for measuring usability and for addressing usability issues. Heuristic evaluation by experts and user-centered testing are typically used to identify usability issues and to ensure satisfactory usability.

However, significant challenges exist, including 1) accuracy of problem identification due to false alarms common in expert evaluation 2) unrealistic evaluation of usability due to differences between the testing environment and the actual usage environment, and 3) increased cost due to the prolonged evolution and maintenance cycles typical for many Web applications. On the other hand, log data routinely kept at Web servers represent actual usage. Such data have been used for usage-based testing and quality assurance and also for understanding user behavior and guiding user interface design.

Server-side logs can be automatically generated by Web servers, with each entry corresponding to a user request. By analyzing these logs, Web workload was characterized and used to suggest performance enhancements for Internet Web servers. Because of the vastly uneven Web traffic, massive user population, and diverse usage environment, coverage-based testing is insufficient to ensure the quality of Web applications. Therefore, server-side logs have been used to construct Web usage models for usage-based Web testing or to automatically generate test cases accordingly to improve test efficiency.

LITRATURE SURVEY

WEB USABILITY PROBE: A TOOL FOR SUPPORTING REMOTE USABILITY EVALUATION OF WEB SITES

PUBLICATION: Human-Computer Interaction—INTERACT 2011. New York, NY, USA: Springer, 2011,pp. 349–357.

AUTHORS: T. Carta, F. Patern`o, and V. F. D. Santana

EXPLANATION:

Usability evaluation of Web sites is still a difficult and time-consuming task, often performed manually. This paper presents a tool that supports remote usability evaluation of Web sites. The tool considers client-side data on user interactions and JavaScript events. In addition, it allows the definition of custom events, giving evaluators the flexibility to add specific events to be detected and considered in the evaluation. The tool supports evaluation of any Web site by exploiting a proxy-based architecture and enables the evaluator to perform a comparison between actual user behavior and an optimal sequence of actions.

SUPPORTING ACTIVITY MODELLING FROM ACTIVITY TRACES

PUBLICATION: Expert Syst., vol. 29, no. 3, pp. 261–275, 2012.

AUTHORS: O. L. Georgeon, A. Mille, T. Bellet, B. Mathern, and F. E. Ritter,

EXPLANATION:

We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach.

TOOLS FOR REMOTE USABILITY EVALUATION OF WEB APPLICATIONS THROUGH BROWSER LOGS AND TASK MODELS

PUBLICATION: Behavior Res.Methods, Instrum., Comput., vol. 35, no. 3, pp. 369–378, 2003

AUTHORS: L. Paganelli and F. Patern`o,

EXPLANATION:

The dissemination of Web applications is extensive and still growing. The great penetration of Web sites raises a number of challenges for usability evaluators. Video-based analysis can be rather expensive and may provide limited results. In this article, we discuss what information can be provided by automatic tools able to process the information contained in browser logs and task models. To this end, we present a tool that can be used to compare log files of user behavior with the task model representing the actual Web site design, in order to identify where users’ interactions deviate from those envisioned by the system design.

SYSTEM ANALYSIS

EXISTING SYSTEM:

Previous studies usability has long been addressed and discussed, when people navigate the Web they often encounter a number of usability issues. This is also due to the fact that Web surfers often decide on the spur of the moment what to do and whether to continue to navigate in a Web site. Usability evaluation is thus an important phase in the deployment of Web applications. For this purpose automatic tools are very useful to gather larger amount of usability data and support their analysis.

Remote evaluation implies that users and evaluators are separated in time and/or space. This is important in order to analyse users in their daily environments and decreases the costs of the evaluation without requiring the use of specific laboratories and asking the users to move. In addition, tools for remote Web usability evaluation should be sufficiently general so that they can be used to analyse user behaviour even when using various browsers or applications developed using different toolkits. We prefer logging on the client-side in order to be able to capture any user-generated events, which can provide useful hints regarding possible usability problems.

Existing approaches have been used to support usability evaluation. An example was WebRemUsine, which was a tool for remote usability evaluation of Web applications through browser logs and task models. Propp and Frorbrig have used task models for supporting usability evaluation of a different type of application: cooperative behaviour of people interacting in smart environments. A different use of models is in the authors discuss how task models can enhance visualization of the usability test log. In our case we do not require the effort of developing models to apply our tool. We only require that the designer provides an example of optimal use associated with each of the relevant tasks. The tool will then compare the logs with the actual use with the optimal log in order to identify deviations, which may indicate potential usability problems.

DISADVANTAGES:

Web navigate used a logger to collect data from a user session test on a Web interface prototype running on a PDA simulator in order to evaluate different types of Web navigation tools and identify the best one for small display devices.

Users were asked to find the answer to specific questions using different types of navigation tools to move from one page to another. A database was used to store users’ actions, but they logged only the answer given by the user to each specific question. Moreover they stored separately every term searched by the user by means of the internal search tool.

Client-side data encounters different challenges regarding the identification of the elements that users are interacting with, how to manage element identification when the page is changed dynamically, how to manage data logging when users are going from one page to another, amongst others. The following are some of the solutions we adopted in order to deal with these issues.

PROPOSED SYSTEM:

We propose a new method to identify navigation related usability problems by comparing Web usage patterns extracted from server logs against anticipated usage represented in some cognitive user models (RQ2). Fig. 1 shows the architecture of our method. It includes three major modules: Usage Pattern Extraction, IUIP Modeling, and Usability Problem Identification. First, we extract actual navigation paths from server logs and discover patterns for some typical events. In parallel, we construct IUIP models for the same events. IUIP models are based on the cognition of user behavior and can represent anticipated paths for specific user-oriented tasks.

Our IUIP models are based on the cognitive models surveyed in Section II, particularly the ACT-R model. Due to the complexity of ACT-R model development and the low-level rule based programming language it relies on we constructed our own cognitive architecture and supporting tool based on the ideas from ACT-R. In general, the user behavior patterns can be traced with a sequence of states and transitions. Our IUIP consists of a number of states and transitions. For a particular goal, a sequence of related operation rules can be specified for a series of transitions. Our IUIP model specifies both the path and the benchmark interactive time (no more than a maximum time) for some specific states (pages). The benchmark time can first be specified based on general rules for common types of Web pages. Humans usually try to complete their tasks in the most efficient manner by attempting to maximize their returns while minimizing the cost.

Typically, experts and novices will have different task performance. Novices need to learn task specific knowledge while performing the task, but experts can complete the task in the most efficient manner. Based on this cognitive mechanism, IUIP models our method is cost-effective. It would be particularly valuable in the two common situations, where an adequate number of actual users cannot be involved in testing and cognitive experts are in short supply. Server logs in our method represent real users’ operations in natural working conditions, and our IUIP models injected with human behavior cognition represent part of cognitive experts’ work. We are currently integrating these modeling and analysis tools into a tool suite that supports measurement, analysis, and overall quality improvement for Web applications.

ADVANTAGES:

1) Logical deviation calculation:

1. a) When the path choice anticipated by the IUIP model is available but not selected, a single deviation is counted.
2. b) Sum up all the above deviations over all the selected user transactions for each page.

2) Temporal deviation calculation:

1. a) When a user spends more time at a specific page than the benchmark specified for the corresponding state in the IUIP model, a single deviation is counted.
2. b) Sum up all the above deviations over all the selected user transactions for each page.

The successive pages related to furniture categories are grouped into a dashed box. The pages with deviations and the unanticipated follow up pages below them are marked with solid rectangular boxes. Those unanticipated follow up pages will not be used themselves for deviation calculations to avoid double counting.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive                       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server :           Apache Tomact Server
• Script :           JSP Script
• Document :           MS-Office 2007

Due to the broadcast nature of radio propagation, wireless transmission can be readily overheard by unauthorized users for interception purposes and is thus highly vulnerable to eavesdropping attacks. To this end, physical-layer security is emerging as a promising paradigm to protect the wireless communications against eavesdropping attacks by exploiting the physical characteristics of wireless channels. This article is focused on the investigation of diversity techniques to improve physical-layer security differently from the conventional artificial noise generation and beamforming techniques, which typically consume additional power for generating artificial noise and exhibit high implementation complexity for beamformer design. We present several diversity approaches to improve wireless physical-layer security, including multiple-input multiple-output (MIMO), multiuser diversity, and cooperative diversity. To illustrate the security improvement through diversity, we propose a case study of exploiting cooperative relays to assist the signal transmission from source to destination while defending against eavesdropping attacks.
We evaluate the security performance of cooperative relay transmission in Rayleigh fading environments in terms of secrecy capacity and intercept probability. It is shown that as the number of relays increases, both the secrecy capacity and intercept probability of cooperative relay transmission improve  significantly, implying there is an advantage in exploiting cooperative diversity to improve physical-layer security against eavesdropping attacks.

In wireless networks, transmission between legitimate users can easily be overheard by an eavesdropper for interception due to the broadcast nature of the wireless medium, making wireless transmission highly vulnerable to eavesdropping attacks. In order to achieve confidential transmission, existing communications systems typically adopt the cryptographic techniques to prevent an eavesdropper from tapping data transmission between legitimate users [1, 2]. By considering symmetric key encryption as an example, the original data (called plaintext) is first encrypted at the source node by using an  encryption algorithm along with a secret key that is shared only with the destination node. Then the encrypted plaintext (also known as ciphertext) is transmitted to the destination, which will decrypt its received ciphertext with the preshared secret key. In this way, even if an eavesdropper overhears the ciphertext transmission, it is still difficult for the eavesdropper to interpret the plaintext from its intercepted ciphertext without the secret key. It is pointed out that ciphertext transmission is not perfectly secure, since the ciphertext can still be decrypted by an eavesdropper through an exhaustive key search, which is also known as a brute-force attack. To this end, physical-layer security is emerging as an alternative paradigm to protect wireless communications against eavesdropping attacks, including brute-force attacks.
Physical-layer security work was pioneered by Wyner in [3], where a discrete memoryless wiretap channel was examined for secure communications in the presence of an eavesdropper. It was proved in [3] that perfectly secure data transmission can be achieved if the channel capacity of the main link (from source to destination) is higher than that of the wiretap link (from source to eavesdropper). Later on, in [4], Wyner’s results were extended from the discrete memoryless wiretap channel to the Gaussian wiretap channel, where a so-called secrecy capacity was developed, and shown as the difference between the channel capacity of the main link and that of the wiretap link. If the secrecy capacity falls below zero, the transmission from source to destination becomes insecure, and the eavesdropper can succeed in intercepting the source transmission (i.e., an intercept event occurs). In order to improve transmission security against eavesdropping attacks, it is of importance to reduce the probability of occurrence of an intercept event (called intercept probability) through enlarging secrecy capacity. However, in wireless communications, secrecy capacity is severely degraded due to the fading effect.

As a consequence, there are extensive works aimed at  increasing the secrecy capacity of wireless communications by exploiting multiple antennas [5] and cooperative relays [6].
Specifically, the multiple-input multiple-output (MIMO) wiretap channel was studied in [7] to enhance the wireless secrecy capacity in fading environments. In [8], cooperative relays were examined for improving the physical-layer security in terms of the secrecy rate performance. A hybrid cooperative beamforming and jamming approach was investigated in [9] to enhance the wireless secrecy capacity, where partial relay nodes are allowed to assist the source transmission to the legitimate destination with the aid of distributed beamforming, while the remaining relay nodes are used to transmit artificial noise to confuse the eavesdropper. More recently, a joint physical-application layer security framework was proposed in [10] for improving the security of wireless multimedia delivery by simultaneously exploiting physical-layer signal processing techniques as well as upper-layer authentication and watermarking methods. In [11], error control coding for secrecy was discussed for achieving the physical-layer security.
Additionally, in [12, 13], physical-layer security was further investigated in emerging cognitive radio networks. At the time of writing, most research efforts are devoted to examining the artificial noise and beamforming techniques to combat eavesdropping attacks, but they consume additional power resources to generating artificial noise and increase the computational complexity in performing beamformer design.
Therefore, this article is motivated to enhance the physicallayer security through diversity techniques without additional power costs, including MIMO, multiuser diversity, and cooperative diversity, aimed at increasing the capacity of the main channel while degrading the wiretap channel. For illustration purposes, we present a case study of exploiting cooperative relays to improve the physical-layer security against eavesdropping attacks, where the best relay is selected and used to participate in forwarding the signal transmission from source to destination. We evaluate the secrecy capacity and intercept probability of the proposed cooperative relay transmission in Rayleigh fading environments. It is shown that with an increasing number of relays, the security performance of cooperative relay transmission significantly improves in terms of secrecy capacity and intercept probability. This confirms the advantage of using cooperative relays to protect wireless communications against eavesdropping attacks.
The remainder of this article is organized as follows. The next section presents the system model of physical-layer security in wireless communications. After that, we focus on the physical-layer security enhancement through diversity techniques, including MIMO, multiuser diversity, and cooperative diversity. For the purpose of illustrating the security improvement through diversity, we present a case study of exploiting cooperative relays to assist signal transmission from source to destination against eavesdropping attacks. Finally, we provide some concluding remarks.

ABSTRACT:

Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting.

Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

INTRODUCTION:

Identity-Based Encryption (IBE) is an interesting alternative to public key encryption, which is proposed to simplify key management in a certificate-based Public Key Infrastructure (PKI) by using human-intelligible identities (e.g., unique name, email address, IP address, etc) as public keys. Therefore, sender using IBE does not need to look up public key and certificate, but directly encrypts message with receiver’s identity.

Accordingly, receiver obtaining the private key associated with the corresponding identity from Private Key Generator (PKG) is able to decrypt such ciphertext. Though IBE allows an arbitrary string as the public key which is considered as appealing advantages over PKI, it demands an efficient revocation mechanism. Specifically, if the private keys of some users get compromised, we must provide a mean to revoke such users from system. In PKI setting, revocation mechanism is realized by appending validity periods to certificates or using involved combinations of techniques.

Nevertheless, the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. As far as we know, though revocation has been thoroughly studied in PKI, few revocation mechanisms are known in IBE setting. In Boneh and Franklin suggested that users renew their private keys periodically and senders use the receivers’ identities concatenated with current time period. But this mechanism would result in an overhead load at PKG. In another word, all the users regardless of whether their keys have been revoked or not, have to contact with PKG periodically to prove their identities and update new private keys. It requires that PKG is online and the secure channel must be maintained for all transactions, which will become a bottleneck for IBE system as the number of users grows.

In presented a revocable IBE scheme. Their scheme is built on the idea of fuzzy IBE primitive but utilizing a binary tree data structure to record users’ identities at leaf nodes. Therefore, key-update efficiency at PKG is able to be significantly reduced from linear to the height of such binary tree (i.e. logarithmic in the number ofusers). Nevertheless, we point out that though the binary tree introduction is able to achieve a relative high performance, it will result in other problems:

1) PKG has to generate a key pair for all the nodes on the path from the identity leaf node to the root node, which results in complexity logarithmic in the number of users in system for issuing a single private key.

2) The size of private key grows in logarithmic in the number of users in system, which makes it difficult in private key storage for users.

3) As the number of users in system grows, PKG has to maintain a binary tree with a large amount of nodes, which introduces another bottleneck for the global system. In tandem with the development of cloud computing, there has emerged the ability for users to buy on-demand computing from cloud-based services such as Amazon’s EC2 and Microsoft’s Windows Azure. Thus it desires a new working paradigm for introducing such cloud services into IBE revocation to fix the issue of efficiency and storage overhead described above. A naive approach would be to simply hand over the PKG’s master key to the Cloud Service Providers (CSPs).

The CSPs could then simply update all the private keys by using the traditional key update technique [4] and transmit the private keys back to unrevoked users. However, the naive approach is based on an unrealistic assumption that the CSPs are fully trusted and is allowed to access the master key for IBE system. On the contrary, in practice the public clouds are likely outside of the same trusted domain of users and are curious for users’ individual privacy. For this reason, a challenge on how to design a secure revocable IBE scheme to reduce the overhead computation at PKG with an untrusted CSP is raised.

In this paper, we introduce outsourcing computation into IBE revocation, and formalize the security definition of outsourced revocable IBE for the first time to the best of our knowledge. We propose a scheme to offload all the key generation related operations during key-issuing and keyupdate, leaving only a constant number of simple operations for PKG and eligible users to perform locally. In our scheme, as with the suggestion in realize revocation through updating the private keys of the unrevoked users. But unlike that work which trivially concatenates time period with identity for key generation/update and requires to re-issue the whole private key for unrevoked users.

We propose a novel collusion-resistant key issuing technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound two sub-components, namely the identity component and the time component. At first, user is able to obtain the identity component and a default time component (i.e., for current time period) from PKG as his/her private key in key-issuing. Afterwards, in order to maintain decryptability, unrevoked users needs to periodically request on keyupdate for time component to a newly introduced entity named Key Update Cloud Service Provider (KU-CSP).

Our scheme does not have to re-issue the whole private keys, but just need to update a lightweight component of it at a specialized entity KU-CSP. We also specify that 1) with the aid of KU-CSP, user needs not to contact with PKG in key-update, and in other words, PKG is allowed to be offline after sending the revocation list to KU-CSP. 2) No secure channel or user authentication is required during key-update between user and KU-CSP. Furthermore, we consider realizing revocable IBE with a semi-honest KU-CSP. To achieve this goal, we present a security enhanced construction under the recently formalized Refereed Delegation of Computation (RDoC) model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction

EXISTING SYSTEM:

• Identity-Based Encryption (IBE) is an interesting alternative to public key encryption, which is proposed to simplify key management in a certificate-based Public Key Infrastructure (PKI) by using human-intelligible identities (e.g., unique name, email address, IP address, etc) as public keys.
• Boneh and Franklin suggested that users renew their private keys periodically and senders use the receivers’ identities concatenated with current time period.
• Hanaoka et al. proposed a way for users to periodically renew their private keys without interacting with PKG.
• Lin et al. proposed a space efficient revocable IBE mechanism from non-monotonic Attribute-Based Encryption (ABE), but their construction requires times bilinear pairing operations for a single decryption where the number of revoked users is.

DISADVANTAGES:

Boneh and Franklin mechanism would result in an overhead load at PKG. In another word, all the users regardless of whether their keys have been revoked or not, have to contact with PKG periodically to prove their identities and update new private keys. It requires that PKG is online and the secure channel must be maintained for all transactions, which will become a bottleneck for IBE system as the number of users grows.

• Boneh and Franklin’s suggestion is more a viable solution but impractical.
• In Hanaoka et al system, however, the assumption required in their work is that each user needs to possess a tamper-resistant hardware device.
• If an identity is revoked then the mediator is instructed to stop helping the user. Obviously, it is impractical since all users are unable to decrypt on their own and they need to communicate with mediator for each decryption.

PROPOSED SYSTEM:

• In this paper, we introduce outsourcing computation into IBE revocation, and formalize the security definition of outsourced revocable IBE for the first time to the best of our knowledge. We propose a scheme to offload all the key generation related operations during key-issuing and keyupdate, leaving only a constant number of simple operations for PKG and eligible users to perform locally.
• In our scheme, as with the suggestion, we realize revocation through updating the private keys of the unrevoked users. But unlike that work which trivially concatenates time period with identity for key generation/update and requires to re-issue the whole private key for unrevoked users, we propose a novel collusion-resistant key issuing technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound two sub-components, namely the identity component and the time component.
• At first, user is able to obtain the identity component and a default time component (i.e., for current time period) from PKG as his/her private key in key-issuing. Afterwards, in order to maintain decryptability, unrevoked users needs to periodically request on keyupdate for time component to a newly introduced entity named Key Update Cloud Service Provider (KU-CSP).

ADVANTAGES:

• Compared with the previous work, our scheme does not have to re-issue the whole private keys, but just need to update a lightweight component of it at a specialized entity KU-CSP.
• We also specify in the aid of KU-CSP, user needs not to contact with PKG in key-update, in other words, PKG is allowed to be offline after sending the revocation list to KU-CSP.
• No secure channel or user authentication is required during key-update between user and KU-CSP.
• Furthermore, we consider to realize revocable IBE with a semi-honest KU-CSP. To achieve this goal, we present a security enhanced construction under the recently formalized Refereed Delegation of Computation (RDoC) model.
• Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor              –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server :           Apache Tomact Server
• Script :           JSP Script
• Document :           MS-Office 2007

ABSTRACT:

Remote data integrity checking is of crucial importance in cloud storage. It can make the clients verify whether their outsourced data is kept intact without downloading the whole data. In some application scenarios, the clients have to store their data on multi-cloud servers. At the same time, the integrity checking protocol must be efficient in order to save the verifier’s cost. From the two points, we propose a novel remote data integrity checking model: ID-DPDP (identity-based distributed provable data possession) in multi-cloud storage. The formal system model and security model are given. Based on the bilinear pairings, a concrete ID-DPDP protocol is designed. The proposed ID-DPDP protocol is provably secure under the hardness assumption of the standard CDH (computational Diffie-Hellman) problem. In addition to the structural advantage of Elimination of certificate management, our ID-DPDP protocol is also efficient and flexible. Based on the client’s authorization, the proposed ID-DPDP protocol can realize private verification, delegated verification and public verification.

SYSTEM ANALYSIS

EXISTING SYSTEM:

The foundations of cloud computing lie in the outsourcing of computing tasks to the third party. Remote data integrity checking is a primitive to address this issue. For the general case, when the client stores his data on multi-cloud servers, the distributed storage and integrity checking are risk. On the other hand, the integrity checking protocol must be efficient in order to make it suitable for capacity-limited end devices. Thus, based on distributed computation, we will study distributed remote data integrity checking model and present the corresponding concrete protocol in multi-cloud storage. And also integrity of user Is not possible in existing system.

DISADVANTAGES:

• The integrity of data is not possible in existing system
• An existing system public verifier does not check the data in multi cloud

PROPOSED SYSTEM:

• First, we analyze the performance of our proposed ID-DPDP protocol from the computation and communication overhead. We compare our ID-DPDP protocol with the other up-to date PDP protocols.. Second, we analyze our proposed ID-DPDP protocol’s properties of flexibility and verification. Third, we give the prototypal implementation of the proposed ID-DPDP protocol.
• The signature relates the client’s identity with his private key. Distributed computing is used to store the client’s data on multi-cloud servers. At the same time, distributed computing is also used to combine the multi-cloud servers’ responses to respond the verifier’s challenge.

ADVANTAGES:

• In our proposed system each client has a private correspond to his identity (i.e.) name, id or any…
• The public verifier allow the user to correspond to his identity (i.e.) private Key

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win 7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server :           Apache Tomact Server
• Script :           JSP Script
• Script :           JSP Script
• Document :           MS-Office 2007

ABSTRACT:

In this paper proposes Searchable Public-Key Ciphertexts with Hidden Structures (SPCHS) for keyword search as fast as possible without sacrificing semantic security of the encrypted keywords. In SPCHS, all keyword-searchable ciphertexts are structured by hidden relations, and with the search trapdoor corresponding to a keyword, the minimum information of the relations is disclosed to a search algorithm as the guidance to find all matching ciphertexts efficiently.

We construct a SPCHS scheme from scratch in which the ciphertexts have a hidden star-like structure. We prove our scheme to be semantically secure in the Random Oracle (RO) model. The search complexity of our scheme is dependent on the actual number of the ciphertexts containing the queried keyword, rather than the number of all ciphertexts.

Finally, we present a generic SPCHS construction from anonymous identity-based encryption and collision-free full-identity malleable Identity-Based Key Encapsulation Mechanism (IBKEM) with anonymity. We illustrate two collision-free full-identity malleable IBKEM instances, which are semantically secure and anonymous, respectively, in the RO and standard models. The latter instance enables us to construct an SPCHS scheme with semantic security in the standard model.

INTRODUCTION:

We start by formally defining the concept of Searchable Public-key Ciphertexts with Hidden Structures (SPCHS) and its semantic security. In this new concept, keywordsearchable ciphertexts with their hidden structures can be generated in the public key setting; with a keyword search trapdoor, partial relations can be disclosed to guide the discovery of all matching ciphertexts. Semantic security is defined for both the keywords and the hidden structures. It is worth noting that this new concept and its semantic security are suitable for keyword-searchable ciphertexts with any kind of hidden structures. In contrast, the concept of traditional PEKS does not contain any hidden structure among the PEKS ciphertexts; correspondingly, its semantic security is only defined for the keywords. Following the SPCHS definition, we construct a simple SPCHS from scratch in the random oracle (RO) model. The scheme generates keyword-searchable ciphertexts with a hidden star-like structure. The search performance mainly depends on the actual number of the ciphertexts containing the queried keyword. For security, the scheme is proven semantically secure based on the Decisional Bilinear DiffieHellman (DBDH) assumption in the RO model.

We build a generic SPCHS construction with IdentityBased Encryption (IBE) and collision-free full-identity malleable IBKEM. The resulting SPCHS can generate keyword-searchable ciphertexts with a hidden star-like structure. Moreover, if both the underlying IBKEM and IBE have semantic security and anonymity (i.e. the privacy of receivers’ identities), the resulting SPCHS is semantically secure. As there are known IBE schemes [4], [5], [6], [7] in both the RO model and the standard model, an SPCHS construction is reduced to collision-free full-identity malleable IBKEM with anonymity. We proposed several IBKEM schemes to construct Verifiable Random Functions2 (VRF). We show that one of these IBKEM schemes is anonymous and collision-free fullidentity malleable in the RO model. We transform this IBE scheme into a collision-free full-identity malleable IBKEM scheme with semantic security and anonymity in the standard model. Hence, this new IBKEM scheme allows us to build SPCHS schemes secure in the standard model with the same search performance as the previous SPCHS construction from scratch in the RO model.

LITRATURE SURVEY

TITLE: FUZZY KEYWORD SEARCH OVER ENCRYPTED DATA IN CLOUD COMPUTING

AUTOHR: Li J., Wang Q., Wang C., Cao N., Ren K., Lou W

PUBLISH:  IEEE INFOCOM 2010, pp. 1-5. (2010)

EXPLANATION:

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users’ searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.

TITLE: ANONYMOUS FUZZY IDENTITY-BASED ENCRYPTION FOR SIMILARITY SEARCH

AUTOHR: Cheung D. W., Mamoulis N., Wong W. K., Yiu S. M., Zhang

PUBLISH: ISAAC 2010. LNCS, vol. 6505, pp. 61-72. Springer, Heidelberg (2010)

EXPLANATION:

The predicate that was studied in the very beginning is “exact keyword matching”. That is, whether the value hidden by the token is equal to the attribute value hidden in the ciphertext. Schemes that only provide data item security are basically “Identity-Based Encryption”. Schemes protecting both the data item and the attributes were initiated in the private-key setting public-key setting. Relationship between and “Anonymous Identity-Based Encryption” was revisited in range query as the predicate was also considered. Boneh et al. devised an Augmented Broadcast Encryption which allows checking if the attribute value falls within a range on encrypted data. Their scheme also provides attribute protection. Then, Boneh and Waters extended it to multi-dimensional range query.

However, there is no practical scheme supporting this predicate with attribute protection in public-key settings investigated this problem in the private-key setting and is IND2-CKA secure. His scheme is in a public-key setting. However, the scheme requires the threshold value t to be fixed in the setup time. Our work is using as a framework provided schemes for handling predicates represented as inner products. Their formulation of using inner products with bounded disjunction is powerful. We show how to reduce inner products to hamming distance similarity comparison predicate, and then derive a slightly different encryption scheme for better performance when considering the inequality case. In our work, we consider the problem of attribute protection in public-key setting. In some applications, people may also want to provide protection to predicate (“the token”), which is inherently unachievable in public-key setting. Note that a predicate encryption supporting inner product in private-key setting has been devised in which can provide predicate privacy

TITLE: TRAPDOOR PRIVACY IN ASYMMETRIC SEARCHABLE ENCRYPTION SCHEMES

AUTOHR: Arriaga A., Tang Q., Ryan P

PUBLISH: AFRICACRYPT 2014. LNCS, vol. 8469, pp. 31-50. Springer, Heidelberg (2014)

EXPLANATION:

Asymmetric searchable encryption allows searches to be carried over ciphertexts, through delegation, and by means of trapdoors issued by the owner of the data. Public Key Encryption with Keyword Search (PEKS) is a primitive with such functionality that provides delegation of exact-match searches. As it is important that ciphertexts preserve data privacy, it is also important that trapdoors do not expose the user’s search criteria. The difficulty of formalizing a security model for trapdoor privacy lies in the verification functionality, which gives the adversary the power of verifying if a trapdoor encodes a particular keyword. In this paper, we provide a broader view on what can be achieved regarding trapdoor privacy in asymmetric searchable encryption schemes, and bridge the gap between previous definitions, which give limited privacy guarantees in practice against search patterns. We propose the notion of Strong Search Pattern Privacy for PEKS and construct a scheme that achieves this security notion.

SYSTEM ANALYSIS

EXISTING SYSTEM:

Existing semantically secure PEKS schemes take search time linear with the total number of all ciphertexts. This makes retrieval from large-scale databases prohibitive. Therefore, more efficient search performance is crucial for practically deploying PEKS schemes. One of the prominent works to accelerate the search over encrypted keywords in the public-key setting enabling search over encrypted keywords to be as effi- cient as the search for unencrypted keywords, such that a ciphertext containing a given keyword can be retrieved in time complexity logarithmic in the total number of all ciphertexts.

This is reasonable because the encrypted keywords can form a tree-like structure when stored according to their binary values. However, deterministic encryption has two inherent limitations. First, keyword privacy can be guaranteed only for keywords that are a priori hardto-guess by the adversary (i.e., keywords with high minentropy to the adversary); second, certain information of a message leaks inevitably via the ciphertext of the keywords since the encryption is deterministic. Hence, deterministic encryption is only applicable in special scenarios.

Observe that a keyword space is usually of no high minentropy in many scenarios. Semantic security is crucial to guarantee keyword privacy in such applications. Thus the linear search complexity of existing schemes is the major obstacle to their adoption. Unfortunately, the linear complexity seems to be inevitable because the server has to scan and test each ciphertext, due to the fact that these ciphertexts (corresponding to the same keyword or not) are indistinguishable to the server.

DISADVANTAGES:

Each sender should be able to generate the keyword-searchable ciphertexts with the hidden star-like structure by the receiver’s public-key; the server having a keyword search trapdoor should be able to disclose partial relations, which is related to all matching ciphertexts. Semantic security is preserved 1) if no keyword search trapdoor is known, all ciphertexts are indistinguishable, and no information is leaked about the structure, and 2) given a keyword search trapdoor, only the corresponding relations can be disclosed, and the matching ciphertexts leak no information about the rest of ciphertexts, except the fact that the rest do not contain the queried keyword.

• The integrity of data is not possible in existing system
• An existing system public verifier does not check the data in multi cloud

PROPOSED SYSTEM:

We propose methods of searchable Public-key Ciphertexts with Hidden Structures (SPCHS) and its semantic security. In this new concept, keywordsearchable ciphertexts with their hidden structures can be generated in the public key setting; with a keyword search trapdoor, partial relations can be disclosed to guide the discovery of all matching ciphertexts. Semantic security is defined for both the keywords and the hidden structures. Following the SPCHS definition, we construct a simple SPCHS from scratch in the random oracle (RO) model. The scheme generates keyword-searchable ciphertexts with a hidden star-like structure. The search performance mainly depends on the actual number of the ciphertexts containing the queried keyword.

We are also interested in providing a generic SPCHS construction to generate keyword-searchable ciphertexts with a hidden star-like structure. Our generic SPCHS is inspired by several interesting observations on Identity-Based Key Encapsulation Mechanism (IBKEM). We build a generic SPCHS construction with IdentityBased Encryption (IBE) and collision-free full-identity malleable IBKEM. The resulting SPCHS can generate keyword-searchable ciphertexts with a hidden star-like structure. Moreover, if both the underlying IBKEM and IBE have semantic security and anonymity (i.e. the privacy of receivers’ identities), the resulting SPCHS is semantically secure. As there are known IBE schemes in both the RO model and the standard model, an SPCHS construction is reduced to collision-free full-identity malleable IBKEM.

ADVANTAGES:

IBKEM schemes to construct Verifiable Random Functions2 (VRF) [8]. We show that one of these IBKEM schemes is anonymous and collision-free fullidentity malleable in the RO model utilized the “approximation” of multilinear maps to construct a standard-model version of Boneh-and-Franklin (BF) IBE scheme.

We transform this IBE scheme into a collision-free full-identity malleable IBKEM scheme with semantic security and anonymity in the standard model. Hence, this new IBKEM scheme allows us to build SPCHS schemes secure in the standard model with the same search performance as the previous SPCHS construction from scratch in the RO model.

• In our proposed system each client has a private correspond to his identity (i.e.) name, id or any…
• The public verifier allow the user to correspond to his identity (i.e.) private Key

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse              –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server                         :           Apache Tomact Server
• Script :           JSP Script
• Document :           MS-Office 2007

ABSTRACT:

Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm.

We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.

INTRODUCTION:

What Is A Social Network?

Wikipedia defines a social network service as a service which “focuses on the building and verifying of online social networks for communities of people who share interests and activities, or who are interested in exploring the interests and activities of others, and which necessitates the use of software.”

A report published by OCLC provides the following definition of social networking sites: “Web sites primarily designed to facilitate interaction between users who share interests, attitudes and activities, such as Facebook, Mixi and MySpace.”

What Can Social Networks Be Used For?

Social networks can provide a range of benefits to members of an organization:

Support for learning: Social networks can enhance informal learning and support social connections within groups of learners and with those involved in the support of learning.

Support for members of an organisation:  Social networks can potentially be used my all members of an organisation, and not just those involved in working with students. Social networks can help the development of communities of practice.

Engaging with others: Passive use of social networks can provide valuable business intelligence and feedback on institutional services (although this may give rise to ethical concerns).

Ease of access to information and applications: The ease of use of many social networking services can provide benefits to users by simplifying access to other tools and applications. The Facebook Platform provides an example of how a social networking service can be used as an environment for other tools.

Common interface: A possible benefit of social networks may be the common interface which spans work / social boundaries. Since such services are often used in a personal capacity the interface and the way the service works may be familiar, thus minimising training and support needed to exploit the services in a professional context.  This can, however, also be a barrier to those who wish to have strict boundaries between work and social activities.

Examples of popular social networking services include:

Facebook: Facebook is a social networking Web site that allows people to communicate with their friends and exchange information. In May 2007 Facebook launched the Facebook Platform which provides a framework for developers to create applications that interact with core Facebook features

MySpace: MySpace is a social networking Web site offering an interactive, user-submitted network of friends, personal profiles, blogs and groups, commonly used for sharing photos, music and videos.

Ning: An online platform for creating social Web sites and social networks aimed at users who want to create networks around specific interests or have limited technical skills.

Twitter: Twitter is an example of a micro-blogging service. Twitter can be used in a variety of ways including sharing brief information with users and providing support for one’s peers.

Note that this brief list of popular social networking services omits popular social sharing services such as Flickr and YouTube.

Opportunities and Challenges

The popularity and ease of use of social networking services have excited institutions with their potential in a variety of areas. However effective use of social networking services poses a number of challenges for institutions including long-term sustainability of the services; user concerns over use of social tools in a work or study context; a variety of technical issues and legal issues such as copyright, privacy, accessibility; etc.

Institutions would be advised to consider carefully the implications before promoting significant use of such services.

Twenty years ago, people typically made friends with others who live or work close to themselves, such as neighbors or colleagues. We call friends made through this traditional fashion as G-friends, which stands for geographical location-based friends because they are influenced by the geographical distances between each other. With the rapid advances in social networks, services such as Facebook, Twitter and Google+ have provided us revolutionary ways of making friends. According to Facebook statistics, a user has an average of 130 friends, perhaps larger than any other time in history. One challenge with existing social networking services is how to recommend a good friend to a user. Most of them rely on pre-existing user relationships to pick friend candidates.

For example, Facebook relies on a social link analysis among those who already share common friends and recommends symmetrical users as potential friends. Unfortunately, this approach may not be the most appropriate based on recent sociology findings. According to these studies, the rules to group people together include: 1) habits or life style; 2) attitudes; 3) tastes; 4) moral standards; 5) economic level; and 6) people they already know. Rather, life styles are usually closely correlated with daily routines and activities. Therefore, if we could gather information on users’ daily routines and activities, we can exploit rule #1 and recommend friends to people based on their similar life styles. This recommendation mechanism can be deployed as a standalone app on smartphones or as an add-on to existing social network frameworks. In both cases, Friendbook can help mobile phone users find friends either among strangers or within a certain group as long as they share similar life styles.

LITRATURE SURVEY:

1) “Probabilistic mining of socio geographic routines from mobile phone data”

AUTHORS:  K. Farrahi and D. Gatica-Perez

There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper, we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT’s Reality Mining project. Some of the human activities discovered with our multimodal data representation include “going out from 7 pm-midnight alone” and “working from 11 am-5 pm with 3-5 other people,” further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework for human routine discovery by predicting missing multimodal phone data at specific times of the day.

1. Collaborative and structural recommendation of friends using weblog-based social network analysis

AUTHORS:  W. H. Hsu, A. King, M. Paradesi, T. Pydimarri, and T. Weninger

In this paper, we address the problem of link recommendation in weblogs and similar social networks. First, we present an approach based on collaborative recommendation using the link structure of a social network and content-based recommendation using mutual declared interests. Next, we describe the application of this approach to a small representative subset of a large real-world social network: the user/community network of the blog service Live Journal. We then discuss the ground features available in Live Journal’s public user information pages and describe some graph algorithms for analysis of the social network. These are used to identify candidates, provide ground truth for recommendations, and construct features for learning the concept of a recommended link. Finally, we compare the performance of this machine learning approach to that of the rudimentary recommender system provided by Live Journal.

1. Understanding Transportation Modes Based on GPS Data for Web Applications.

AUTHORS:  Y. Zheng, Y. Chen, Q. Li, X. Xie, and W.-Y. Ma.

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, people’s transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user’s mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users’ transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person’s direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based post-processing to further improve the inference performance. This post-processing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. 1) Our approach can effectively segment trajectories containing multiple transportation modes. 2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. 3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.

1. Online friend recommendation through personality matching and collaborative filtering

AUTHORS: L. Bian and H. Holtzman

Most social network websites rely on people’s proximity on the social graph for friend recommendation. In this paper, we present Matchmaker, a collaborative filtering friend recommendation system based on personality matching. The goal of Matchmaker is to leverage the social information and mutual understanding among people in existing social network connections, and produce friend recommendations based on rich contextual data from people’s physical world interactions. Matchmaker allows users’ network to match them with similar TV characters, and uses relationships in the TV programs as parallel comparison matrix to suggest to the users friends that have been voted to suit their personality the best. The system’s ranking schema allows progressive improvement on the personality matching consensus and more diverse branching of users’ social network connections. Lastly, our user study shows that the application can also induce more TV content consumption by driving users’ curiosity in the ranking process.

SYSTEM ANALYSIS:

EXISTING SYSTEM:

Most of the friend suggestions mechanism relies on pre-existing user relationships to pick friend candidates. For example, Facebook relies on a social link analysis among those who already share common friends and recommends symmetrical users as potential friends. The rules to group people together include:

• Habits or life style
• Attitudes
• Tastes
• Moral standards
• Economic level; and
• People they already know.

Apparently, rule #3 and rule #6 are the mainstream factors considered by existing recommendation systems.

DISADVANTAGES:

• Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life

PROPOSED SYSTEM:

• A novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs.
• By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity.
• We model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm.
• Similarity metric to measure the similarity of life styles between users, and calculate users’
• Impact in terms of life styles with a friend-matching graph.
• We integrate a linear feedback mechanism that exploits the user’s feedback to improve recommendation accuracy.

ADVANTAGES:

• Recommend potential friends to users if they share similar life styles.
• The feedback mechanism allows us to measure the satisfaction of users, by providing a user interface that allows the user to rate the friend list

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor                       –    SVGA

SOFTWARE REQUIREMENTS:

JAVA

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server :           Apache Tomact Server
• Script :           JSP Script
• Document :           MS-Office 2007

• ABSTRACT:

In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, where power savings are introduced by consolidating resources in the network and data centers. We model our approach in an IP over WDM network using mixed integer linear programming (MILP). The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices.

The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under an energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model. We also compare the different approaches adopting energy efficient data center power profile. Furthermore, we study the impact of delay and node location constraints on the energy efficiency of virtual network embedding. We also show how VNE can impact the design of optimally located data centers for minimal power consumption in cloud networks. Finally, we examine the power savings and spectral efficiency benefits that VNE offers in optical orthogonal division multiplexing networks.

• INTRODUCTION:

The ever growing uptake of cloud computing as a widely accepted computing paradigm calls for novel architectures to support QoS and energy efficiency in networks and data centers. Estimates indicate that in the long term, if current trends continue, the annual energy bill paid by data center operators will exceed the cost of equipment. Given the ecological and economic impact, both academia and industry are focusing efforts on developing energy efficient paradigms for cloud computing. In, the authors stated that the success of future cloud networks where clients are expected to be able to specify the data rate and processing requirements for hosted applications and services will greatly depend on network virtualization. The form of cloud computing service offering under study here is Infrastructure as a Service (IaaS). IaaS is the delivery of virtualized and dynamically scalable computing power, storage and networking on demand to clients on a pay as you go basis.

Network virtualization allows multiple heterogeneous virtual network architectures (comprising virtual nodes and links) to coexist on a shared physical platform, known as the substrate network which is owned and operated by an infrastructure provider (InP) or cloud service provider whose aim is to earn a profit from leasing network resources to its customers (Service Providers (SPs)). It provides scalability, customised and on demand allocation of resources and the promise of efficient use of network resources. Network virtualization is therefore a strong proponent for the realization of an efficient IaaS framework in cloud networks. InPs should have a resource allocation framework that reserves and allocates physical resources to elements such as virtual nodes and virtual links. Resource allocation is done using a class of algorithms commonly known as “virtual network embedding (VNE)” algorithms. The dynamic mapping of virtual resources onto the physical hardware maximizes the benefits gained from existing hardware. The VNE problem can be either Offline or Online. In offline problems all the virtual network requests (VNRs) are known and scheduled in advance while for the online problem, VNRs arrive dynamically and can stay in the network for an arbitrary duration.

Both online and offline problems are known to be NPhard. With constraints on virtual nodes and links, the offline VNE problem can be reduced to the NP-hard multiway separator problem, as a result, most of the work done in this area has focused on the design of heuristic algorithms and the use of networks with minimal complexity when solving mixed integer linear programming (MILP) models. Network virtualization has been proposed as an enabler of energy savings by means of resource consolidation. In all these proposals, the VNE models and/or algorithms do not address the link embedding problem as a multi-layer problem spanning from the virtualization layer through the IP layer and all the way to the optical layer. Except for the authors in, the others do not consider the power consumption of network ports/links as being related to the actual traffic passing through them.

On the contrary, we take a very generic, detailed and accurate approach towards energy efficient VNE (EEVNE) where we allow the model to decide the optimum approach to minimize the total network and data centers server power consumption. We consider the granular power consumption of various network elements that form the network engine in backbone networks as well as the power consumption in data centers. We develop a MILP model and a real-time heuristic to represent the EEVNE approach for clouds in IP over WDM networks with data centers. We study the energy efficiency considering two different power consumption profiles for servers in data centers; An energy inefficient power profile and an energy efficient power profile. Our work also investigates the impact of location and delay constraints in a practical enterprise solution of VNE in clouds. Furthermore we show how VNE can impact the design problem of optimally locating data centers for minimal power consumption in cloud networks.

• LITRATURE SURVEY:

RESOURCE ALLOCATION IN A NETWORK-BASED CLOUD COMPUTING ENVIRONMENT: DESIGN CHALLENGES

AUTHOR: M. A. Sharkh, M. Jammal, A. Shami, and A. Ouda

PUBLISH: IEEE Commun. Mag., vol. 51, no. 11, pp. 46–52, 2013.

EXPLANATION:

Cloud computing is a utility computing paradigm that has become a solid base for a wide array of enterprise and end-user applications. Providers offer varying service portfolios that differ in resource configurations and provided services. A comprehensive solution for resource allocation is fundamental to any cloud computing service provider. Any resource allocation model has to consider computational resources as well as network resources to accurately reflect practical demands. Another aspect that should be considered while provisioning resources is energy consumption. This aspect is getting more attention from industrial and government parties. Calls for the support of green clouds are gaining momentum. With that in mind, resource allocation algorithms aim to accomplish the task of scheduling virtual machines on the servers residing in data centers and consequently scheduling network resources while complying with the problem constraints. Several external and internal factors that affect the performance of resource allocation models are introduced in this article. These factors are discussed in detail, and research gaps are pointed out. Design challenges are discussed with the aim of providing a reference to be used when designing a comprehensive energy-aware resource allocation model for cloud computing data centers.

DISTRIBUTED ENERGY EFFICIENT CLOUDS OVER CORE NETWORKS

AUTHOR: A. Q. Lawey, T. E. H. El-Gorashi, and J. M. H. Elmirghani

PUBLISH: IEEE J. Lightw. Technol., vol. 32, no. 7, pp. 1261–1281, Jan. 2014.

EXPLANATION:

In this paper, we introduce a framework for designing energy efficient cloud computing services over non-bypass IP/WDM core networks. We investigate network related factors including the centralization versus distribution of clouds and the impact of demand, content popularity and access frequency on the clouds placement, and cloud capability factors including the number of servers, switches and routers and amount of storage required in each cloud. We study the optimization of three cloud services: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications. First, we develop a mixed integer linear programming (MILP) model to optimize cloud content delivery services. Our results indicate that replicating content into multiple clouds based on content popularity yields 43% total saving in power consumption compared to power un-aware centralized content delivery. Based on the model insights, we develop an energy efficient cloud content delivery heuristic, DEER-CD, with comparable power efficiency to the MILP results. Second, we extend the content delivery model to optimize StaaS applications. The results show that migrating content according to its access frequency yields up to 48% network power savings compared to serving content from a single central location. Third, we optimize the placement of VMs to minimize the total power consumption. Our results show that slicing the VMs into smaller VMs and placing them in proximity to their users saves 25% of the total power compared to a single virtualized cloud scenario. We also develop a heuristic for real time VM placement (DEER-VM) that achieves comparable power savings.

Reducing power consumption in embedding virtual infrastructures

AUTHOR: B. Wang, X. Chang, J. Liu, and J. K. Muppala

PUBLISH: c. IEEE Globecom Workshops, Dec. 3–7, 2012, pp. 714–718.

EXPLANATION:

Network virtualization is considered to be not only an enabler to overcome the inflexibility of the current Internet infrastructure but also an enabler to achieve an energy-efficient Future Internet. Virtual network embedding (VNE) is a critical issue in network virtualization technology. This paper explores a joint power-aware node and link resource allocation approach to handle the VNE problem with the objective of minimizing energy consumption. We first present a generalized power consumption model of embedding a VN. Then we formulate the problem as a mixed integer program and propose embedding algorithms. Simulation results demonstrate that the proposed algorithms perform better than the existing algorithms in terms of the power consumption in the overprovisioned scenarios.

SYSTEM ANALYSIS

EXISTING SYSTEM:

Existing methods of disaster-resilient optical datacenter networks through integer linear programming (ILP) and heuristics addressed content placement, routing, and protection of network and content for geographically distributed cloud services delivered by optical networks models and heuristics are developed to minimize delay and power consumption of clouds over IP/WDM networks. The authors of exploited anycast routing by intelligently selecting destinations and routes for users traffic served by clouds over optical networks, as opposed to unicast traffic, while switching off unused network elements. A unified, online, and weighted routing and scheduling algorithm is presented in for a typical optical cloud infrastructure considering the energy consumption of the network and IT resources.

In the authors provided an optimization-based framework, where the objective functions range from minimizing the energy and bandwidth cost to minimizing the total carbon footprint subject to QoS constraints. Their model decides where to build a data center, how many servers are needed in each datacenter and how to route requests. In we built a MILP model to study the energy efficiency of public cloud for content delivery over non-bypass IP/WDM core networks. The model optimizes clouds external factors including the location of the cloud in the IP/WDM network and whether the cloud should be centralized or distributed and cloud internal capability factors including the number of servers, internal LAN switches, routers, and amount of storage required in each cloud.

DISADVANTAGES:

(i) Studying the impact of small content (storage) size on the energy efficiency of cloud content delivery

(ii) Developing a real time heuristic for energy aware content delivery based on the content delivery model insights,

(iii) Extending the content delivery model to study the Storage as a Service (StaaS) application,

(iv) ILP model for energy aware cloud VM placement and designing a heuristic to mimic the model behaviour in real time.

PROPOSED SYSTEM:

We developed a MILP model which attempts to minimize the bandwidth cost of embedding a VNR. In the virtual network embedding energy aware (VNE-EA) model minimized the energy consumption by imposing the notion that the power consumption is minimized by switching off substrate links and nodes. The authors also assume that the power saved in switching off a substrate link is the same as the power saved by switching off a substrate node.

In the authors assumed that the power consumption in the network is insensitive to the number of ports used. They also seek to minimize the number of active working nodes and links. Botero and Hesselbach have proposed a model for energy efficiency using load balancing and have also developed a dynamic heuristic that reconfigures the embedding for energy efficiency once it is performed. They have implemented and evaluated their MILP models and heuristic algorithms using the ALEVIN Framework. The ALEVIN Framework is a good tool for developing, comparing and analyzing VNE algorithms.

The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices.

The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model.

ADVANTAGES:

We are however unable to compare our model and heuristic to the implemented algorithms on the platform for the following reasons:

1. Our input parameters are not compatible to the existing models and algorithms on the platform. Extensive extensions to the algorithms and models would be needed for them to include the optical layer. Our parameters include among others; the distance in km between links for us to determine the number of EDFA’s or Regenerators needed on a link, the wavelength rate, the number of wavelengths in a fiber, the power consumption of EDFAs, transponders, regenerators, router ports, optical cross connects, multiplexers, de-multiplexers, etc.
2. The assumptions made in the calculation of power in our model and the models on the platform are different. We define the power consumption to its fine granularity to include power consumed due to traffic on each element that forms the network engine. One of our main contributions in this work is the inclusion of the optical layer in link embedding which is currently not supported by any of the algorithms on the ALEVIN platform.

We developed a generalized power consumption model of embedding a VNR and formulated it as a MILP model; however, they also assumed that the power consumption of the network ports is independent of traffic. In the authors propose a trade-off between maximizing the number of VNRs that can be accommodated by the InP and minimizing the energy cost of the whole system. They propose embedding requests in regions with the lowest electricity cost.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor              –    SVGA

SOFTWARE REQUIREMENTS:

JAVA

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Document :           MS-Office 2007

Abstract—Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme.
The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO”  perations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

INTRODUCTION
The cloud computing treats computing as a utility and leases out the computing and storage capacities to the public individuals [1], [2], [3]. In such a framework, the individual can remotely store her data on the cloud server, namely data outsourcing, and then make the cloud data open for public access through the cloud server. This represents a more scalable, low-cost and stable way for public data access because of the scalability and high efficiency of cloud servers, and therefore is favorable to small enterprises.

Note that the outsourced data may contain sensitive privacy information. It is often necessary to encrypt the private data before transmitting the data to the cloud servers [4], [5]. The data encryption, however, would significantly lower the usability of data due to the difficulty of searching over the encrypted data [6]. Simply encrypting the data may still cause other security concerns. For instance, Google Search uses SSL (Secure Sockets Layer) to encrypt the connection between search user and Google server when private data, such as documents and emails, appear in the search results [7].
However, if the search user clicks into another website from the search results page, that website may be able to identify the search terms that the user has used.
On addressing above issues, the searchable encryption (e.g., [8], [9], [10]) has been recently developed as a fundamental approach to enable searching over encrypted cloud data, which proceeds the following operations. Firstly, the data owner needs to generate several keywords according to the outsourced data. These keywords are then encrypted and stored at the cloud server. When a search user needs to access the outsourced data, it can select some relevant keywords and send the ciphertext of the selected keywords to the cloud server. The cloud server then uses the ciphertext to match the outsourced encrypted keywords, and lastly returns the matching results to the search user. To achieve the similar
search efficiency and precision over encrypted data as that of plaintext keyword search, an extensive body of research has been developed in literature. Wang et al. [11] propose a ranked keyword search scheme which considers the relevance scores of keywords. Unfortunately, due to using order-preserving
encryption (OPE) [12] to achieve the ranking property, the proposed scheme cannot achieve unlinkability of trapdoor.
Later, Sun et al. [13] propose a multi-keyword text search scheme which considers the relevance scores of keywords and utilizes a multidimensional tree technique to achieve efficient search query. Yu et al. [14] propose a multi-keyword top-k retrieval scheme which uses fully homomorphic encryption to
encrypt the index/trapdoor and guarantees high security. Cao et al. [6] propose a multi-keyword ranked search (MRSE), which applies coordinate machine as the keyword matching rule, i.e., return data with the most matching keywords.
Although many search functionalities have been developed in previous literature towards precise and efficient searchable encryption, it is still difficult for searchable encryption to achieve the same user experience as that of the plaintext search, like Google search. This mainly attributes to following
two issues. Firstly, query with user preferences is very popular in the plaintext search [15], [16]. It enables personalized search and can more accurately represent user’s requirements, but has
not been thoroughly studied and supported in the encrypted data domain. Secondly, to further improve the user’s experience on searching, an important and fundamental function is to enable the multi-keyword search with the comprehensive logic operations, i.e., the “AND”, “OR” and “NO” operations
of keywords. This is fundamental for search users to prune the searching space and quickly identify the desired data.
Cao et al. [6] propose the coordinate matching search scheme (MRSE) which can be regarded as a searchable encryption scheme with “OR” operation. Zhang et al. [17] propose a conjunctive keyword search scheme which can be regarded as a searchable encryption scheme with “AND” operation with
the returned documents matching all keywords. However, most existing proposals can only enable search with single logic operation, rather than the mixture of multiple logic operations on keywords, which motivates our work. In this work, we address above two issues by developing two Fine-grained Multi-keyword Search (FMS) schemes over encrypted cloud data. Our original contributions can be summarized in three aspects as follows:
• We introduce the relevance scores and the preference factors of keywords for searchable encryption. The relevance scores of keywords can enable more precise returned results, and the preference factors of keywords represent the importance of keywords in the search keyword set specified by search users and correspondingly enables personalized search to cater to specific user preferences. It thus further improves the search functionalities and user experience.
• We realize the “AND”, “OR” and “NO” operations in the multi-keyword search for searchable encryption. Compared with schemes in [6], [13] and [14], the proposed scheme can achieve more comprehensive functionality and lower query complexity.
• We employ the classified sub-dictionaries technique to enhance the efficiency of the above two schemes. Extensive experiments demonstrate that the enhanced schemes can achieve better efficiency in terms of index building, trapdoor generating and query in the comparison with schemes in [6], [13] and [14].

ABSTRACT:

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data.

In this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy.

This framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

GOAL OF THE PROJECT:

Efficient and privacy-preserving multi-keyword ranked search over encrypted mobile cloud data via blind storage system, the EMRS has following design goals:

• Multi-Keyword Ranked Search: To meet the requirements for practical uses and provide better user experience, the EMRS should not only support multi-keyword search over encrypted mobile cloud data, but also achieve relevance-based result ranking.
• Search Efficiency: Since the number of the total documents may be very large in a practical situation, the EMRS should achieve sublinear search with better search efficiency.
• Confidentiality and Privacy Preservation: To prevent the cloud server from learning any additional information about the documents and the index, and to keep search users’ trapdoors secret, the EMRS should cover all the security requirements that we introduced above.

INTRODUCTION

Mobile cloud computing gets rid of the hardware limitation of mobile devices by exploring the scalable and virtualized cloud storage and computing resources, and accordingly is able to provide much more powerful and scalable mobile services to users. In mobile cloud computing, mobile users typically outsource their data to external cloud servers, e.g., iCloud, to enjoy a stable, low-cost and scalable way for data storage and access. However, as outsourced data typically contain sensitive privacy information, such as personal photos, emails, etc., which would lead to severe confidentiality and privacy violations, if without efficient protections. It is therefore necessary to encrypt the sensitive data before outsourcing them to the cloud. The data encryption, however, would result in salient difficulties when other users need to access interested data with search, due to the difficulties of search over encrypted data.

This fundamental issue in mobile cloud computing accordingly motivates an extensive body of research in the recent years on the investigation of searchable encryption technique to achieve efficient searching over outsourced encrypted data. A collection of research works have recently been developed on the topic of multi-keyword search over encrypted data. Propose a symmetric searchable encryption scheme which achieves high efficiency for large databases with modest scarification on security guarantees. Propose a multi-keyword search scheme supporting result ranking by adopting k-nearest neighbors (kNN) technique. Propose a dynamic searchable encryption scheme through blind storage to conceal access pattern of the search user.

In order to meet the practical search requirements, search over encrypted data should support the following three functions.

First, the searchable encryption schemes should support multi-keyword search, and provide the same user experience as searching in Google search with different keywords; single-keyword search is far from satisfactory by only returning very limited and inaccurate search results. Second, to quickly identify most relevant results, the search user would typically prefer cloud servers to sort the returned search results in a relevance-based order ranked by the relevance of the search request to the documents. In addition, showing the ranked search to users can also eliminate the unnecessary network traffic by only sending back the most relevant results from cloud to search users.

Third, as for the search efficiency, since the number of the documents contained in a database could be extraordinarily large, searchable encryption schemes should be efficient to quickly respond to the search requests with minimum delays.

In contrast to the theoretical benefits, most of the existing proposals, however, fail to offer sufficient insights towards the construction of full functioned searchable encryption as described above. As an effort towards the issue, in this paper, we propose an efficient multi-keyword ranked search (EMRS) scheme over encrypted mobile cloud data through blind storage.

Our main contributions can be summarized as follows:

• We introduce a relevance score in searchable encryption to achieve multi-keyword ranked search over the encrypted mobile cloud data. In addition to that, we construct an efficient index to improve the search efficiency.
• By modifying the blind storage system in the EMRS, we solve the trapdoor unlinkability problem and conceal access pattern of the search user from the cloud server.
• We give thorough security analysis to demonstrate that the EMRS can reach a high security level including confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Moreover, we implement extensive experiments, which show that the EMRS can achieve enhanced efficiency in the terms of functionality and search efficiency compared with existing proposals.

LITRATURE SURVEY

SYSTEM ANALYSIS

EXISTING SYSTEM:

Existing works built various types of secure index and corresponding index-based keyword matching algorithms to improve search efficiency. All these works only support the search of single keyword. Subsequent works extended the search capability to multiple, conjunctive or disjunctive, keywords search. However, they support only exact keyword matching. Misspelled keywords in the query will result in wrong or no matching. Very recently, a few works extended the search capability to approximate keyword matching (also known as fuzzy search). These are all for single keyword search, with a common approach involving expanding the index file by covering possible combinations of keyword misspelling so that a certain degree of spelling error, measured by edit distance, can be tolerated. Although a wild-card approach is adopted to minimize the expansion of the resulting index file, for a l-letter long keyword to tolerate an error up to an edit distance of d, the index has to be expanded times.

Thus, it is not scalable as the storage complexity increases exponentially with the increase of the error tolerance. To support multi-keyword search, the search algorithm will have to run multiple rounds To date, efficient multi-keyword fuzzy search over encrypted data remains a challenging problem. We want to point out that the efforts on search over encrypted data involve not only information retrieval techniques such as advanced data structures used to represent the searchable index, and efficient search algorithms that run over the corresponding data structure, but also the proper design of cryptographic protocols to ensure the security and privacy of the overall system. Although single keyword search and fuzzy search have been implemented separately, a combination of the two does not lead to a secure and efficient single keyword fuzzy search scheme.

DISADVANTAGES:

The large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. The searchable encryption focuses on single keyword search or Boolean keyword search, and rarely differentiates the search results.

• Single-keyword search without ranking
• Boolean- keyword search without ranking
• Single-keyword similarity search with ranking

PROPOSED SYSTEM:

Propose a symmetric searchable encryption scheme which achieves high efficiency for large databases with modest scarification on security guarantees. Propose a multi-keyword search scheme supporting result ranking by adopting k-nearest neighbors (kNN) technique. Propose a dynamic searchable encryption scheme through blind storage to conceal access pattern of the search user.

We propose the detailed EMRS. Since the encrypted documents and index z are both stored in the blind storage system, we would provide the general construction of the blind storage system. Moreover, since the EMRS aims to eliminate the risk of sharing the key that is used to encrypt the documents with all search users and solve the trapdoor unlinkability problem in Naveed’s scheme.

We modify the construction of blind storage and leverage ciphertext policy attribute-based encryption (CP-ABE) technique in the EMRS. However, specific construction of CP-ABE is out of scope of this paper and we only give a simple indication here. The notations of this paper are shown in Table 1. The EMRS consists of the following phases: System Setup, Construction of Blind Storage, Encrypted Database Setup, Trapdoor Generation, Efficient and Secure Search, and Retrieve Documents from Blind Storage.

ADVANTAGES:

In this paper, we propose an efficient multi-keyword ranked search (EMRS) scheme over encrypted mobile cloud data through blind storage.

Our main contributions can be summarized as follows:

• We introduce a relevance score in searchable encryption to achieve multi-keyword ranked search over the encrypted mobile cloud data. In addition to that, we construct an efficient index to improve the search efficiency.
• By modifying the blind storage system in the EMRS, we solve the trapdoor unlinkability problem and conceal access pattern of the search user from the cloud server.
• We give thorough security analysis to demonstrate that the EMRS can reach a high security level including confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Moreover, we implement extensive experiments, which show that the EMRS can achieve enhanced efficiency in the terms of functionality and search efficiency compared with existing proposals

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed       –    1 GHz
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

SOFTWARE REQUIREMENTS:

• Operating System        :           Windows XP or Win7
• Front End       :           JAVA JDK 1.7
• Back End :           MYSQL Server
• Server :           Apache Tomact Server
• Script :           JSP Script
• Document :           MS-Office 2007

Abstract—Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model – the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval  applications.

INTRODUCTION
GRAPH-BASED ranking models have been deeply studied and widely applied in information retrieval area. In this paper, we focus on the problem of applying a novel and efficient graph-based model for contentbased image retrieval (CBIR), especially for out-of-sample retrieval on large scale databases.
Traditional image retrieval systems are based on keyword search, such as Google and Yahoo image search. In these systems, a user keyword (query) is matched with the context around an image including the title, manual annotation, web document, etc. These systems don’t utilize information from images. However these systems suffer many problems, such as shortage of the text information and inconsistency of the meaning of the text and image. Content-based image retrieval is a considerable choice to overcome these difficulties. CBIR has drawn a great attention in the past two decades [1]–[3]. Different from traditional keyword search systems, CBIR systems utilize the low-level features, including global features (e.g., color moment, edge histogram, LBP [4]) and local features (e.g., SIFT [5]), automatically extracted from images. A great amount of researches have been performed for designing more informative low-level features to represent images, or better metrics (e.g., DPF [6]) to measure the perceptual similarity, but their performance is restricted by many conditions and is sensitive to the data. Relevance feedback [7] is a useful tool for interactive CBIR. User’s high level perception is captured by dynamically updated weights based on the user’s feedback.
Most traditional methods focus on the data features too much but they ignore the underlying structure information, which is of great importance for semantic discovery, especially when the label information is unknown. Many databases have underlying cluster or manifold structure.
Under such circumstances, the assumption of label consistency is reasonable [8], [9]. It means that those nearby data points, or points belong to the same cluster or manifold, are very likely to share the same semantic label. This phenomenon is extremely important to explore the semantic relevance when the label information is unknown. In our opinion, a good CBIR system should consider images’ lowlevel features as well as the intrinsic structure of the image database.
Manifold Ranking (MR) [9], [10], a famous graph-based ranking model, ranks data samples with respect to the intrinsic geometrical structure collectively revealed by a large number of data. It is exactly in line with our consideration. MR has been widely applied in many applications, and shown to have excellent performance and feasibility on a variety of data types, such as the text [11], image [12], [13], and video[14]. By taking the underlying structure into account, manifold ranking assigns each data sample a relative ranking score, instead of an absolute pairwise similarity as traditional ways. The score is treated as a similarity metric defined on the manifold, which is more meaningful to capturing the semantic relevance degree. He et al. [12] firstly applied MR to CBIR, and significantly improved image retrieval performance compared with state-of-the-art algorithms.
However, manifold ranking has its own drawbacks to handle large scale databases – it has expensive computational cost, both in graph construction and ranking computation stages. Particularly, it is unknown how to handle an out-of-sample query (a new sample) efficiently under the existing framework. It is unacceptable to recompute the model for a new query. That means, original manifold ranking is inadequate for a real world CBIR system, in which the user provided query is always an out-of-sample.
In this paper, we extend the original manifold ranking and propose a novel framework named Efficient Manifold Ranking (EMR). We try to address the shortcomings of manifold ranking from two perspectives: the first is scalable graph construction; and the second is efficient computation, especially for out-of-sample retrieval. Specifically, we build an anchor graph on the database instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The model has two separate stages: an offline stage for building (or learning) the ranking model and an online stage for handling a new query. With EMR, we can handle a database with 1 million images and do the online retrieval in a short time. To the best of our knowledge, no previous manifold ranking based algorithm has run out-of-sample retrieval on a database in this scale.