A Novel Framework For User Customizable Privacy Preserving Search

The objective of the Personalized web search (PWS) is to provide an effective and efficient search results, which are tailor mode for individual user needs. we build user profiles based on user preference and these profiles are then used to re-rank the search results and rank the order of user-examined results.User privacy can be protected without affecting the personalized search quality. However, users are troubled, with exposing personal preference information to search engines has become a major limitation for profile based personalized web search.The Privacy-preserving personalized web search framework is called UPS framework which can generalize profiles for each query according to user-specific privacy requirements. .In general, there is a tradeoff between the search quality and the level of privacy protection achieved from generalization. Effective generalization algorithms namely GreedyDP and GreedyIL are used to support the runtime profiling. Experiments are conducted on real web search data show that the algorithms are effective in enhancing the stability of the search quality and avoids the unnecessary exposure of the user profile. DOI: 10.17762/ijritcc2321-8169.150313

CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions

By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re- identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results

Client Side Privacy Protection Using Personalized Web Search

AbstractWe are providing a Client-side privacy protection for personalized web search.. Any PWS captures user profiles in a hierarchical taxonomy. The system is performing online generalization on user profiles to protect the personal privacy without compromising the search quality and attempt to improve the search quality with the personalization utility of the user profile. On other side they need to hide the privacy contents existing in the user profile to place the privacy risk under control. User privacy can be provided in form of protection like without compromising the personalized search quality. In general we are working for a trade off between the search quality and the level of privacy protection achieved from generalization

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

A Privacy Protection in Personalized Web Search for Knowledge Mining: A Survey

The web search engines (e.g. Google, Yahoo etc.) help the users to find required useful information on the World Wide Web (WWW). But it has become increasingly difficult to get the expected results from the web search engine because contentsare available in web is very vast and ambiguous.Due to tremendous data opportunities in the internet, the privacy protection is very essential to preserve user search behaviors and their profiles. In this paper system present a novel protocol specially designed to protect the users’ privacy in front of web search profiling. Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. System proposed two greedy algorithms namely GreedyDP and GreedyIL. These two algorithms are used for runtime generalization.The proposed protocol preserves the privacy of the individuals who deal with a web search engine.System provides a distorted user profile to the web search engine. It offers implementation details and computational and communication results that show that the proposed protocol improves the existing solutions in terms of query delay

Privacy Protection in Web Search

This paper presents web search has demonstrated in improving the quality of various search services on the internet, user reluctance to disclose the private information during search has become major barrier for the wide proliferation of password. Protection in password authentication model user preferences as hierarchical user profiles, a password framework know as user profile search that can adaptively generalize profile by search query while respecting user specified privacy requirements. Our work provides utility of personalization and the privacy risk of exposing the generalized profile using Greedy algorithm is a method for deciding whether personalizing a query is efficient

Web Prediction Mechanism for User Personalized Search

Personalized net search (PNS) has incontestible its effectiveness in up the standard of assorted search services on the web. However, evidences show that users� reluctance to disclose their non-public data throughout search has become a serious barrier for the wide proliferation of PNS. We have a tendency to study privacy protection in PNS applications that model user preferences as graded user profiles. We have a tendency to propose a PNS framework referred to as UPS (User customizable Privacy-preserving Search) that may adaptively generalize profiles by queries whereas respecting user specified privacy necessities. Our runtime generalization aims at hanging a balance between 2 prognostic metrics that assess the utility of personalization and therefore the privacy risk of exposing the generalized profile. We have a tendency to gift 2 greedy algorithms, specifically GreedyDP and GreedyIL, for runtime generalization. We have a tendency to additionally give a web prediction mechanism for deciding whether or not personalizing a question is useful. intensive experiments demonstrate the effectiveness of our framework. The experimental results additionally reveal that GreedyIL considerably outperforms GreedyDP in terms of potency

Porqpine: a peer-to-peer search engine

In this paper, we present a fully distributed and collaborative search engine for web pages: Porqpine. This system uses a novel query-based model and collaborative filtering techniques in order to obtain user-customized results. All knowledge about users and profiles is stored in each user node?s application. Overall the system is a multi-agent system that runs on the computers of the user community. The nodes interact in a peer-to-peer fashion in order to create a real distributed search engine where information is completely distributed among all the nodes in the network. Moreover, the system preserves the privacy of user queries and results by maintaining the anonymity of the queries? consumers and results? producers. The knowledge required by the system to work is implicitly caught through the monitoring of users actions, not only within the system?s interface but also within one of the most popular web browsers. Thus, users are not required to explicitly feed knowledge about their interests into the system since this process is done automatically. In this manner, users obtain the benefits of a personalized search engine just by installing the application on their computer. Porqpine does not intend to shun completely conventional centralized search engines but to complement them by issuing more accurate and personalized results.Postprint (published version

Preserving Privacy for User Profling in Personalized Web Search

As the internet content is growing exponentially, the users of search providers demand their search result to be accurate as per their requirement. In such case Personalized Web Search is one of the options available to the user that present search result as per the users information available in the form of user pro?le. The major barrier for Personalized Web Search is the unwillingness of user to share their personal information. All the personal information of user is collected during search process and a hierarchical pro?le based on users preference is created. We propose a client side framework which can be adapted by any PWS that creates users pro?le on the client side and respect users privacy speci?ed by user during the search process. Also, the generalizing algorithm used during search process for generalizing user pro?le is discussed in this paper