Proof of Ownership In Deduplicated Storage With Mobile Device Efficiency

by

Cloud storage such as Dropbox and Bitcasa is one of the most popular cloud services. Currently, with the prevalence of mobile cloud computing, users can even collaboratively edit the newest version of documents and synchronize the newest files on their smart mobile devices. A remarkable feature of current cloud storage is its virtually infinite storage. To support unlimited storage, the cloud storage provider uses data deduplication techniques to reduce the data to be stored and therefore reduce the storage expense. Moreover, the use of data deduplication also helps significantly reduce the need for bandwidth and therefore improve the user experience. Nevertheless, in spite of the above benefits, data deduplication has its inherent security weaknesses. Among them, the most severe is that the adversary may have an unauthorized file downloading via the file hash only. In this article we first review the previous solutions and identify their performance weaknesses. Then we propose an alternative design that achieves cloud server efficiency and especially mobile device efficiency.

1.2 INTRODUCTION

Mobile devices have become prevalent in recent years, and mobile computing has been a growing trend. Meanwhile, cloud computing is definitely the biggest revolution in recent decades. Many tasks, such as document editing and file backup, have been shifted from end devices to the cloud. Therefore, with the convergence of mobile computing and cloud computing, along with the recent development of the 5G communication standard that establishes more reliable and faster communication channels, mobile cloud computing (MCC) could be a rapidly growing field that deserves to be investigated and explored.

Deduplicated Storage in Mobile Cloud Computing Among cloud services, cloud storage with the capability of file backup and synchronization could be the most popular service that enables mobile users to access their files everywhere. Dropbox (https://www.dropbox.com/) and Bitcasa (https://www.bitcasa.com/) are two examples that offer easy-to-use file backup and synchronization services. Several remarkable features of such cloud storage can be identified. It has high availability, which means that the user’s data will be replicated over cloud servers worldwide and is guaranteed to be accessible whenever the user has the need. It has the flexibility in a pay-as-you-go model, which means that the user can gain additional storage immediately whenever the user is willing to make an extra payment. The most important feature is that it has virtually infinite storage space, which means that the user can backup whatever he/she wants to be uploaded to the cloud. A renowned example is Bitcasa, which offers “unlimited storage” that enables the user to upload virtually everything. Offering infinite storage space might cause a severe economic burden on the cloud storage provider.

However, a technique called data deduplication helps significantly reduce the cost of storage. Data deduplication has been widely implemented by cloud storage providers including Dropbox and Bitcasa. According to the report in [8] (http://www.snia.org), the use of data deduplication in business applications may reduce the data to be stored and thus achieve disk and bandwidth savings of more than 90 percent. The power of data deduplication is achieved by avoiding storing the same file multiple times. The storage saving is more obvious especially when the popular multimedia contents such as music and movies are considered. The replicated contents create an additional storage need the first time they are uploaded, but create no extra storage need for subsequent uploads. In addition to storage saving, if the data content has been in the storage, then the replicated content has no need to be transmitted, achieving bandwidth saving. Data deduplication can be categorized as two types depending on where the deduplication take places: server (cloud) side deduplication and client (user) side deduplication. Server side deduplication is simple: the server, after receiving the file, checks whether it already has a copy in storage. The server discards the received file if it does, or creates a new file in the storage if it does not.

We can see that server side deduplication does not produce bandwidth saving because the server performs the deduplication after the file has been received. On the other hand, client side deduplication adopts a more aggressive method: the user calculates and sends the hash of the file before uploading the file. Upon receiving the hash, the server checks whether the hash is already in storage. The user is asked to send nothing and the server associates the user with the existing file if so. The user is asked to upload the file otherwise. An illustrative example is shown in Fig. 2, where user 1 first uploads files F1 and F2 in Fig. 2a. Then the cloud knows from the hashes h(F1) and h(F2) sent by user 2 that there has been a copy of F1 in storage and sends a positive Acknowledgment and negative Acknowledgment to user 2. User 2, according to Acknowledgments, sends only F3, saving the transmission of F1. Public cloud storage services (e.g. Dropbox and Bitcasa) are more likely to adopt client side deduplication because of its storage and bandwidth savings. In particular, in addition to the reduced storage requirement, the client side deduplication can also reduce the need for file transmission, allowing the reduction of waiting time for users and energy consumption for the server. We particularly mention that even with the increased bandwidth of the coming 5G communication standard, the data rate of wireless links is still not compatible to that of wired links. Thus, if we consider the mobile devices accessing cloud storage services, client side deduplication becomes an inevitable technique for MCC applications.

1.3 LITRATURE SURVEY

DUPLESS: SERVERAIDED ENCRYPTION FOR DEDUPLICATED STORAGE

AUTHOR: M. Bellare, S. Keelveedhi, and T. Ristenpart

PUBLISH: Proc. 22nd USENIX Conf. Sec. Symp., 2013, pp. 179–194.

EXPLANATION:

Cloud storage service providers such as Dropbox, Mozy, and others perform deduplication to save space by only storing one copy of each file uploaded. Should clients conventionally encrypt their files, however, savings are lost. Message-locked encryption (the most prominent manifestation of which is convergent encryption) resolves this tension. However it is inherently subject to brute-force attacks that can recover files falling into a known set. We propose an architecture that provides secure deduplicated storage resisting brute-force attacks, and realize it in a system called DupLESS. In DupLESS, clients encrypt under message-based keys obtained from a key-server via an oblivious PRF protocol. It enables clients to store encrypted data with an existing service, have the service perform deduplication on their behalf, and yet achieves strong confidentiality guarantees. We show that encryption for deduplicated storage can achieve performance and space savings close to that of using the storage service with plaintext data.

FAST AND SECURE LAPTOP BACKUPS WITH ENCRYPTED DE-DUPLICATION

AUTHOR: P. Anderson and L. Zhang

PUBLISH: Proc. 24th Int. Conf. Large Installation Syst. Admin., 2010, pp. 29–40.

EXPLANATION:

Many people now store large quantities of personal and corporate data on laptops or home computers. These often have poor or intermittent connectivity, and are vulnerable to theft or hardware failure. Conventional backup solutions are not well suited to this environment, and backup regimes are frequently inadequate. This paper describes an algorithm which takes advantage of the data which is common between users to increase the speed of backups, and reduce the storage requirements. This algorithm supports client-end per-user encryption which is necessary for confidential personal data. It also supports a unique feature which allows immediate detection of common subtrees, avoiding the need to query the backup system for every file. We describe a prototype implementation of this algorithm for Apple OS X, and present an analysis of the potential effectiveness, using real data obtained from a set of typical users. Finally, we discuss the use of this prototype in conjunction with remote cloud storage, and present an analysis of the typical cost savings.

SECURE DEDUPLICATION WITH EFFICIENT AND RELIABLE CONVERGENT KEY MANAGEMENT

AUTHOR:  J. Li, X. Chen, M. Li, J. Li, P. Lee, and W. Lou

PUBLISH: IEEE Trans. Parallel Distrib. Syst., http://oi.ieeecomputersociety.org/10.1109/TPDS.2013.284, 2013

EXPLANATION:

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Data de duplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth.  Previous de duplication systems cannot support differential authorization duplicate check, which is important in many applications. In such an authorized de duplication system, each user is issued a set of privileges during system initialization Each file uploaded to the cloud is also bounded by a set of privileges to specify which kind of users is allowed to perform the duplicate check and access the files.

Before submitting his duplicate check request for a file, the user needs to take this file and his own privileges as inputs. The user is able to find a duplicate f or this file if and only if there is a copy of this file and a matched privilege stored in cloud. Traditional de duplication systems based on convergent encryption, although providing confidentiality to some extent; do not support the duplicate check with differential privileges. In other words, no differential privileges have been considered in the de duplication based on convergent encryption technique. It seems to be contradicted if we want to realize both de duplication and differential authorization duplicate check at the same time.

2.1.1 DISADVANTAGES:

  • De duplication systems cannot support differential authorization duplicate check.
  • One critical challenge of cloud storage services is the management of the ever increasing volume of data.
  • Users’ sensitive data are susceptible to both insider and outsider attacks.
  • Sometimes de duplication impossible.


2.2 PROPOSED SYSTEM:

We propose an alternative design that strikes a balance between server side efficiency and user side efficiency. Before introducing the scheme’s details, we present two observations. First, the POW schemes in are I/O efficient at the server side because the Merkle tree root can be thought of as a compact summary of the file. Therefore, there is no need for the cloud to access the disk to retrieve the file. Second, the user side is computationally efficient in three s-POW schemes because the user is simply required only to answer a few bits of the file. With the above two observations, our design strategy is to have a probabilistic data structure for the compact summary of the file, in contrast to the deterministic data structure, Merkle hash tree, in the POW schemes. The query challenge is also modified as random blocks, in contrast to the random bits in s-POW schemes. An overview of the proposed POW scheme goes as follows.

2.2.1 ADVANTAGES:

POW scheme such as the bandwidth requirement, I/O overhead at both user and server sides, and the computation overhead at both sides concern the performance, the second is less known in the POW design. More specifically, cloud storage usually has a storage hierarchy: the memory (primary storage) and disk (secondary storage).

The execution of a POW scheme might require the user and cloud to access the file stored in the disk multiple times. The server might also need to keep the verification object in either the memory or the disk to verify the user’s claim.

The above all might result in a huge amount of I/O delay because of the access time gap between the memory and disk. In this article we focus only on the abuse of a file hash to gain the ownership of the file and aim to design a POW scheme with minimum performance overhead.

  • To prevent unauthorized access, a secure proof of ownership (POW) protocol is also needed to provide the proof that the user indeed owns the same file when a duplicate is found.
  • It makes overhead to minimal compared to the normal convergent encryption and file upload operations.
  • Data confidentiality is maintained.
  • Secure compared to proposed techniques

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

  • Speed                                      –    1.1 GHz
    • RAM                                       –    256 MB (min)
    • Hard Disk                               –   20 GB
    • Floppy Drive                           –    1.44 MB
    • Key Board                              –    Standard Windows Keyboard
    • Mouse                                     –    Two or Three Button Mouse
    • Monitor                                   –    SVGA

 

2.3.2 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

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

  • The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system
  • The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
  • DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
  • DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

  1. All processes must have at least one data flow in and one data flow out.
  2. All processes should modify the incoming data, producing new forms of outgoing data.
  3. Each data store must be involved with at least one data flow.
  4. Each external entity must be involved with at least one data flow.
  5. A data flow must be attached to at least one process.


3.1 ARCHITECTURE DIAGRAM


 

3.2 DATAFLOW DIAGRAM

USER:

1

ADMIN:


UML DIAGRAMS:

3.2 USE CASE DIAGRAM:


3.3 CLASS DIAGRAM:


3.4 SEQUENCE DIAGRAM:

SENDER USER:

 RECEIVER USER:


3.5 ACTIVITY DIAGRAM:

SENDER LOGIN:

RECEIVER LOGIN:

CHAPTER 4

4.0 IMPLEMENTATION:

MOBILE CLOUD COMPUTING:

Mobile Cloud Computing (MCC) is the combination of cloud computing, mobile computing and wireless networks to bring rich computational resources to mobile users, network operators, as well as cloud computing providers. The ultimate goal of MCC is to enable execution of rich mobile applications on a plethora of mobile devices, with a rich user experience. MCC provides business opportunities for mobile network operators as well as cloud providers. “A rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle.

ARCHITECTURE:

MCC uses computational augmentation approachesby which resource-constraint mobile devices can utilize computational resources of varied cloud-based resources. In MCC, there are four types of cloud-based resources, namely distant immobile clouds, proximate immobile computing entities, proximate mobile computing entities, and hybrid (combination of the other three models). Giant clouds such as Amazon EC2 are in the distant immobile groups whereas cloudlet or surrogates are member of proximate immobile computing entities. Smartphones, tablets, handheld devices, and wearable computing devices are part of the third group of cloud-based resources which is proximate mobile computing entities.

DIAGRAM:

In the MCC landscape, an amalgam of mobile computing, cloud computing, and communication networks (to augment smartphones) creates several complex challenges such as Mobile Computation Offloading, Seamless Connectivity, Long WAN Latency, Mobility Management, Context-Processing, Energy Constraint, Vendor/data Lock-in, Security and Privacy, Elasticity that hinder MCC success and adoption.

Although significant research and development in MCC is available in the literature, efforts in the following domains are still lacking:

  • Architectural issues: Reference architecture for heterogeneous MCC environment is a crucial requirement for unleashing the power of mobile computing towards unrestricted ubiquitous computing.
  • Energy-efficient transmission: MCC requires frequent transmissions between cloud platform and mobile devices, due to the stochastic nature of wireless networks, the transmission protocol should be carefully designed.
  • Context-awareness issues: Context-aware and socially-aware computing are inseparable traits of contemporary handheld computers. To achieve the vision of mobile computing among heterogeneous converged networks and computing devices, designing resource-efficient environment-aware applications is an essential need.
  • Live VM migration issues: Executing resource-intensive mobile application via Virtual Machine (VM) migration-based application offloading involves encapsulation of application in VM instance and migrating it to the cloud, which is a challenging task due to additional overhead of deploying and managing VM on mobile devices.
  • Mobile communication congestion issues: Mobile data traffic is tremendously hiking by ever increasing mobile user demands for exploiting cloud resources which impact on mobile network operators and demand future efforts to enable smooth communication between mobile and cloud endpoints.
  • Trust, security, and privacy issues: Trust is an essential factor for the success of the burgeoning MCC paradigm.

PROOF OF OWNERSHIP:

An even more severe and direct security threat from using deduplicated cloud storage is that the adversary may gain the ownership of files by only eavesdropping on file hashes. A closer look at client side deduplication can find that anyone in possession of the file hash can gain ownership of the file by uploading the file hash. More specifically, the cloud considers receiving a store request for a file already in the storage, avoids the redundant file transmission, and then adds the user as an additional owner of the file. An illustrative example is shown in Fig. 3d. Such a situation is apparently undesirable because in theory the adversary cannot infer the file content via the hash.

However, in this case, once the adversary knows the hash, it is able to download the entire file content. On the other hand, in practice, the user considers the hash unharmful and in some cases publishes the hashes as timestamps. However, the publicly available hashes can be abused to gain the file. This security weakness comes from using the static and short piece of information (hash) as a way of claiming file ownership. Motivated by this observation, Halevi et al. [10] introduce the notion of proof of ownership (POW). A POW scheme is jointly executed by the cloud and user such that the user can prove to the cloud that it is indeed in possession of the file.

4.1 ALGORITHM:

PUBLIC KEY INFRASTRUCTURE (PKI) AND PRIVATE KEY GENERATOR (PKG):

In cryptography, the ElGamal encryption system is an asymmetric key encryption algorithm for public-key cryptography which is based on the Diffie–Hellman key exchange. It was described by Taher Elgamal in 1985. ElGamal encryption is used in the free GNU Privacy Guard software, recent versions of PGP, and other cryptosystems. The DSA (Digital Signature Algorithm) is a variant of the ElGamal signature scheme, which should not be confused with ElGamal encryption. The security of the ElGamal scheme depends on the properties of the underlying group  as well as any padding scheme used on the messages.

If the computational Diffie–Hellman assumption (CDH) holds in the underlying cyclic group , then the encryption function is one-way. If the decisional Diffie–Hellman assumption (DDH) holds in , then ElGamal achieves semantic security. Semantic security is not implied by the computational Diffie–Hellman assumption alone. See decisional Diffie–Hellman assumption for a discussion of groups where the assumption is believed to hold.

To achieve chosen-ciphertext security, the scheme must be further modified, or an appropriate padding scheme must be used. Depending on the modification, the DDH assumption may or may not be necessary.

Other schemes related to ElGamal which achieve security against chosen ciphertext attacks have also been proposed. The Cramer–Shoup cryptosystem is secure under chosen ciphertext attack assuming DDH holds for. Its proof does not use the random oracle model. Another proposed scheme is DHAES whose proof requires an assumption that is weaker than the DDH assumption.

4.2 MODULES:

SECURE USER MODULES:

DEDUPLICATED STORAGE:

CHECK DEDUPLICATES:

APPLY POW SCHEME:

SECURE SEND KEY:

4.3 MODULE DESCRIPTION:

SECURE USER MODULES:

In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first.

  • Registration
  • File View
  • Encryption
  • Download
  • Upload Files
  • Encrypt and save to cloud

DEDUPLICATED STORAGE:

Client side deduplication incurs its own security weaknesses. First, the privacy of the file existence in the cloud may be compromised because the adversary may try to upload the candidate files to see whether the deduplication takes place. If the deduplication takes place, this will be an indica tor of the file’s existence. Otherwise, the adversary may infer the file’s nonexistence. The situation becomes even worse when we consider the low-entropy files because the adversary may exhaustively create different files and upload the hashes to check the file’s existence. For example, a curious colleague may query his/her manager’s salary by uploading different salary sheets because the sheets are of a similar form, restricting the number of file contents to be tested.

CHECK DEDUPLICATES:

Data deduplication can be categorized as two types depending on where the deduplication take places: server (cloud) side deduplication and client (user) side deduplication. Server side deduplication is simple: the server, after receiving the file, checks whether it already has a copy in storage. The server discards the received file if it does, or creates a new file in the storage if it does not. We can see that server side deduplication does not produce bandwidth saving because the server performs the deduplication after the file has been received. On the other hand, client side deduplication adopts a more aggressive method: the user calculates and sends the hash of the file before uploading the file. Upon receiving the hash, the server checks whether the hash is already in storage. The user is asked to send nothing and the server associates the user with the existing file if so. The user is asked to upload the file otherwise. An illustrative example is shown in Fig. 2, where user 1 first uploads files F1 and F2 in Fig. 2a.

Then the cloud knows from the hashes h(F1) and h(F2) sent by user 2 that there has been a copy of F1 in storage and sends a positive Acknowledgment and negative Acknowledgment to user 2. User 2, according to Acknowledgments, sends only F3, saving the transmission of F1. Public cloud storage services (e.g. Dropbox and Bitcasa) are more likely to adopt client side deduplication because of its storage and bandwidth savings. In particular, in addition to the reduced storage requirement, the client side deduplication can also reduce the need for file transmission, allowing the reduction of waiting time for users and energy consumption for the server. We particularly mention that even with the increased bandwidth of the coming 5G communication standard, the data rate of wireless links is still not compatible to that of wired links. Thus, if we consider the mobile devices accessing cloud storage services, client side deduplication becomes an inevitable technique for MCC applications.

APPLY POW SCHEME:

The POW schemes in performance very well on the server side since only a small size index (tree root) needs to be stored in the main memory. However, the proof of ownership is achieved by the user sending an authentication path of size O(log |f|) to the cloud, resulting in more communication overhead and computation load on the cloud. The I/O overhead of the user side is also increased, compared to the POW schemes in, because the user needs to retrieve the entire file. On the other extreme, although the s-POW schemes in have great computation and I/O efficiency in the user side, its I/O burden on the cloud is significantly increased since the cloud is required to retrieve random bits from the secondary storage.

In this article we propose an alternative design that strikes a balance between server side efficiency and user side efficiency. Before introducing the scheme’s details, we present two observations. First, the POW schemes in are I/O efficient at the server side because the Merkle tree root can be thought of as a compact summary of the file. Therefore, there is no need for the cloud to access the disk to retrieve the file. Second, the user side is computationally efficient in three s-POW schemes because the user is simply required only to answer a few bits of the file. With the above two observations, our design strategy is to have a probabilistic data structure for the compact summary of the file, in contrast to the deterministic data structure, Merkle hash tree, in the POW schemes. The query challenge is also modified as random blocks, in contrast to the random bits in s-POW schemes. An overview of the proposed POW scheme goes as follows.

SECURE SEND KEY:

Once the key request was received, the sender can send the key or he can decline it. With this key and request id which was generated at the time of sending key request the receiver can decrypt the message.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are 

  • ECONOMICAL FEASIBILITY
  • TECHNICAL FEASIBILITY
  • SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:     

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

 

5.1.2 TECHNICAL FEASIBILITY   

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.

This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

Description Expected result
Test for application window properties. All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations. All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

5.1.2 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

Description Expected result
Test for all modules. All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group. The result after execution should give the accurate result.


5.1. 3 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

  • Load testing
  • Performance testing
  • Usability testing
  • Reliability testing
  • Security testing

5.1.4 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Description Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. Should designate another active node as a Server.


5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

Description Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.   Should handle large input values, and produce accurate result in a  expected time.  


5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

Description Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. In case of failure of  the server an alternate server should take over the job.


5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

  Description Expected result
Checking that the user identification is authenticated. In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers. The peers should know group key in the same group.


5.1.8 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

Description Expected result
Exercise all logical decisions on their true and false sides. All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds. All the loops must be finite.
Exercise internal data structures to ensure their validity. All the data structures must be valid.


5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

Description Expected result
To check for incorrect or missing functions. All the functions must be valid.
To check for interface errors. The entire interface must function normally.
To check for errors in a data structures or external data base access. The database updation and retrieval must be done.
To check for initialization and termination errors. All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

 

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

 

The Java Programming Language

 

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

  • Simple
    • Architecture neutral
    • Object oriented
    • Portable
    • Distributed     
    • High performance
    • Interpreted     
    • Multithreaded
    • Robust
    • Dynamic
    • Secure     

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

6.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

  • The Java Virtual Machine (Java VM)
  • The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

  • The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
  • Applets: The set of conventions used by applets.
  • Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
  • Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
  • Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
  • Software components: Known as JavaBeansTM, can plug into existing component architectures.
  • Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
  • Java Database Connectivity (JDBCTM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

 

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

  • Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
  • Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
  • Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
  • Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
  • Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure JavaTM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
  • Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
  • Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

 

6.5 ODBC:

 

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

6.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

 

6.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

  1. Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

  • Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

  • Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

  • Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple                                     Architecture-neutral

Object-oriented                       Portable

Distributed                              High-performance

Interpreted                              Multithreaded

Robust                                     Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

6.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

 

6.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

6.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

6.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

 

6.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

7.0 APPENDIX

7.1 SAMPLE SCREEN SHOTS:

7.2 SAMPLE SOURCE CODE:

CHAPTER 8

8.1 CONCLUSION AND FUTURE:

We propose an alternative POW design on the problem of unauthorized file downloading in deduplicated cloud storage. In our design, the use of probabilistic data structure, the Bloom filter, primarily contributes to the overhead reduction. Since the Bloom filter has been used widely in various applications and is easy to be implemented, our proposed POW scheme is considered realistic and can be deployed in real-world cloud storage services. Despite the use of the Bloom filter in reducing the I/O needs, the size of the Bloom filter may grow with the number of files stored in the cloud. The Bloom filter may also be of a huge size so that it needs to be partitioned and part of it needs to be stored in the disk. Thus, one possible future research focus is to develop a more succinct data structure or devise a new index mechanism such that the index (the Bloom filter in this article) can be fit into the memory even in the case of a huge number of files in the cloud.