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

Abstract

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.