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.
1.1 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.
1.2 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.
1.3
LITRATURE SURVEY
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 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.
2.1.1 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.
2.2 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.
2.2.1 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
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
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:
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 CLASS DIAGRAM:
3.4 SEQUENCE DIAGRAM:
3.5
ACTIVITY DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
EMRS
SCHEME (EFFICIENT MULTI-KEYWORD RANKED SEARCH):
4.1 ALGORITHM
CP-ABE ENCRYPTION ALGORITHM:
The data owner builds the encrypted database as follows:
Step 1: The data owner computes the d-dimension
relevance vector p = (p1, p2, · · · pd ) for each document using the TF-IDF
weighting technique, where pj for j ∈
(1, 2 · · · d) represents the weighting of keyword ωj in document di . Then,
the data owner extends the p to a (d+2)-dimension vector p ∗
. The (d+1)-th entry of p ∗ is set to a random
number ε and the (d+2)-th entry is set to 1. We would let ε follow a normal
distribution N(µ, σ2 ) [11]. For each document di , to compute the encrypted
relevance vector, the data owner encrypts the associated extended relevance
vector p ∗ using the secret key M1, M2 and S.
First, the data owner chooses a random number r and splits the extended
relevance vector p ∗ into two
(d+2)-dimension vectors p 0 and p 00 using the vector S. For the j-th item in p
∗
, set
Step 2: For each document di in D, set the document into blocks of mb bits each. For each block, there is a header H(idi) indicating that this block belongs to document di . And the sizei of the document is contained in the header of the first block of di . Then, for each document di , the data owner chooses a 192-bit key Ki for the algorithm Enc(). More precisely, for each block B[j] of the document di , where j represents the index number of this block, compute the Ki ⊕ 8(j) as the key for the encryption of this block. Since each block has a unique index number, the blocks of the same document are encrypted with different keys. The document di contains sizei encrypted blocks and the first block of the document di with index number j is as
Finally, the data owner encrypts all the documents and writes them to the blind storage system using the B.Build function. Step 3: To enable efficient search over the encrypted documents, the data owner builds the index z. First, the data owner defines the access policy υi for each document di . We denote the result of attribute-based encryption using access policy υi as ABEυi (). The data owner initializes z to an empty array indexed by all keywords. Then, the index z can be constructed as shown in Algorithm 1.
4.2 MODULES:
SEARCHABLE ENCRYPTION CP-ABE:
MULTI-KEYWORD RANKED SEARCH:
BLIND STORAGE SYSTEM:
EMRS
SECURITY REQUIRMENTS:
4.3 MODULE DESCRIPTION:
SEARCHABLE ENCRYPTION CP-ABE:
In ciphertext policy attribute-based encryption (CP-ABE), ciphertexts are created with an access structure (usually an access tree) which defines the access policy.A user can decrypt the data only if the attributes embedded in his attribute keys satisfy the access policy in the ciphertext. In CP-ABE, the encrypter holds the ultimate authority of the access policy. The documents are encrypted by the traditional symmetric cryptography technique before being outsourced to the cloud server. Without a correct key, the search user and cloud server cannot decrypt the documents. As for index confidentiality, the relevance vector for each document is encrypted using the secret key M1, M2, and S. And the descriptors of the documents are encrypted using CP-ABE technique. Thus, the cloud server can only use the index z to retrieve the encrypted relevance vectors without knowing any additional information, such as the associations between the documents and the keywords. And only the search user with correct attribute keys can decrypt the descriptor ABE_i (idijjKijjx) to get the document id and the associated symmetric key. Thus, the confidentiality of documents and index can be well protected.
MULTI-KEYWORD RANKED SEARCH:
Multi-keyword rankedsearch 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.
BLIND STORAGE SYSTEM:
A blind storage system is built on the cloud server to support adding, updating and deleting documents and concealing the access pattern of the search user from the cloud server. In the blind storage system, all documents are divided into fixed-size blocks. These blocks are indexed by a sequence of random integers generated by a document-related seed. In the view of a cloud server, it can only see the blocks of encrypted documents uploaded and downloaded. Thus, the blind storage system leaks little information to the cloud server. Specifically, the cloud server does not know which blocks are of the same document, even the total number of the documents and the size of each document. Moreover, all the documents and index can be stored in the blind storage system to achieve a searchable encryption scheme.
EMRS SECURITY REQUIRMENTS:
EMRS, we consider the cloud server to be curious but honest which means it executes the task assigned by the data owner and the search user correctly. However, it is curious about the data in its storage and the received trapdoors to obtain additional information. Moreover, we consider the Knowing Background model in the EMRS, which allows the cloud server to know more background information of the documents such as statistical information of the keywords.
Specifically, the EMRS aims to provide the following four security requirements:
• Confidentiality of Documents and Index: Documents and index should be encrypted before being outsourced to a cloud server. The cloud server should be prevented from prying into the outsourced documents and cannot deduce any associations between the documents and keywords using the index.
• Trapdoor Privacy: Since the search user would like to keep her searches from being exposed to the cloud server, the cloud server should be prevented from knowing the exact keywords contained in the trapdoor of the search user.
• Trapdoor Unlinkability: The trapdoors should not be linkable, which means the trapdoors should be totally different even if they contain the same keywords. In other words, the trapdoors should be randomized rather than determined. The cloud server cannot deduce any associations between two trapdoors.
• Concealing Access Pattern of the
Search User: Access pattern is the sequence of the searched results. In the
EMRS, the access pattern should be totally concealed from the cloud server.
Specifically, the cloud server cannot learn the total number of the documents
stored on it nor the size of the searched document even when the search user retrieves
this document from the cloud server.
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
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.
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:
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:
Java technology is both a programming language and a platform.
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.
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:
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.
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 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.
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:
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.
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.
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.
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.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
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.
| Java Program |
| Compilers |
| Interpreter |
| My Program |
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
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 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 supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
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.
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.
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.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
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”.
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.
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.
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.
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.
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
In this paper, we have proposed a multi-keyword ranked search scheme to enable accurate, efficient and secure search over encrypted mobile cloud data. Security analysis have demonstrated that proposed scheme can effectively achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Extensive performance evaluations have shown that the proposed scheme can achieve better efficiency in terms of the functionality and computation overhead compared with existing ones. For the future work, we will investigate on the authentication and access control issues in searchable encryption technique.