Privacy Protection in Personalized Web Search Via Taxonomy Structure

Web search engine has long become the most important portal for ordinary people looking for useful information on the web. User might experience failure when search engine return irrelevance information due to enormous variety of user's context and ambiguity of text. The Existing System failed to resist ambiguity of text. Our Proposed System aim at removing ambiguity of text and provide the relevance information to the User. We learn privacy protection in PWS applications that model user preferences as hierarchical user profiles (via taxonomy Structure). We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user-specified privacy requirements via taxonomy structure. 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. For runtime generalization greedy algorithms GreedyDP and GreedyIL are used. For deciding whether to personalizing a query is beneficial online mechanism is provided.