09-04-2012, 02:47 PM
i need a explanation of this project wt is abstract,proposed system ,exiting system.and model of this project give me a little bit explain about that project[/size][/font]
09-04-2012, 02:47 PM
i need a explanation of this project wt is abstract,proposed system ,exiting system.and model of this project give me a little bit explain about that project[/size][/font]
18-05-2012, 10:44 AM
to get information about the topic "fuzzy keyword search over encrypted data in cloud computing " full report ppt and related topic refer the link bellow http://project-seminars.com/attachment.php?aid=18361 https://seminarproject.net/Thread-fuzzy-...-computing
09-08-2012, 03:24 PM
Fuzzy Keyword Search over Encrypted Data in Cloud Computing
final fuzzy keyword.doc (Size: 1.54 MB / Downloads: 179) Abstract: As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users’ searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search. Fuzzy keyword search design three ways, first, design general search engine to avoid google, yahoo search engine drawbacks. Second, search personalized profile based on user query. Third one is search relation based result with help of keyword and property matching. And finally compare the relation based search result and existing search result. Existing: 1. searches for the web pages of a person with a given name constitute a notable fraction of queries to Web search engines. 2. A query would normally return web pages related to several namesakes, who happened to have the queried name, leaving the burden of disambiguating and collecting pages relevant to a particular word (from among the namesakes) on the user. 3. Many dynamically generated sites are not indexable by search engines; this phenomenon is known as the invisible web. 4. Some search engines do not order the results by relevance, but rather according to how much money the sites have paid them. 5. Some sites use tricks to manipulate the search engine to display them as the first result returned for some keywords. This can lead to some search results being polluted, with more relevant links being pushed down in the result list. Proposed: 1. To explore new mechanism for constructing storage efficient fuzzy keyword sets; 2. to design efficient and effective fuzzy search scheme based on the constructed fuzzy keyword sets; 3. To validate the security of the proposed scheme. 4. We develop web Search approach that clusters web pages based on their association to different people. 5. Our method exploits a variety of semantic information extracted from web pages, such as named entities and hyperlinks, to disambiguate among namesakes referred to on the web pages. 6. We demonstrate the effectiveness of our approach by testing the efficiency of the disambiguation algorithms and its impact on person search. 7. Our system uses word variants or stemming technology , which not only searches for the words present in the user query but also for similar words. This is implemented by domain independent technologies like thesaurus matching as well as by the use of Semantic Web and ontology technologies. Implement user profile searching: Personalized web search system, which can learn a user's preference implicitly and then generate the user profile automatically. When the user inputs query keywords, more personalized expansion words are generated by the proposed algorithm, and then these words together with the query keywords are submitted to a popular search engine such as Baidu or Google. These expansion words can help search engines retrieval information for a user according to his/her implicit search intentions, and return different search results to different users who input the same keywords. Implement relation based searching With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. Next generation Web architecture, represented by Semantic Web, provides the layered architecture possibly allowing to overcome this limitation. Several search engines have been proposed, which allow to increase information retrieval accuracy by exploiting a key content of Semantic Web resources, that is relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper we propose a relation-based page rank algorithm to be used in conjunction with Semantic Web search engines that simply relies on information which could be extracted from user query and annotated resource. Relevance is measured as the probability that retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definition. • Web Clients A web client consists of two parts: (1) dynamic web pages containing various types of markup language (HTML, XML, and so on), which are generated by web components running in the web tier, and (2) a web browser, which renders the pages received from the server. A web client is sometimes called a thin client. Thin clients usually do not query databases, execute complex business rules, or connect to legacy applications. When we use a thin client, such heavyweight operations are off-loaded to enterprise beans executing on the J2EE server, where they can leverage the security, speed, services, and reliability of J2EE server-side technologies. • Applets A web page received from the web tier can include an embedded applet. An applet is a small client application written in the Java programming language that executes in the Java virtual machine installed in the web browser. However, client systems will likely need the Java Plug-in and possibly a security policy file in order for the applet to successfully execute in the web browser. Web components are the preferred API for creating a web client program because no plug-ins or security policy files are needed on the client systems. Also, web components enable cleaner and more modular application design because they provide a way to separate applications programming from web page design. Personnel involved in web page design thus do not need to understand Java programming language syntax to do their jobs.
30-08-2012, 07:04 PM
plz provide me execution of this project.It is of windows application not a web application.
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