Abstract: The quality of various search services on the Internet is effectively improved by using personalized web search (PWS). Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, evidences show that user’s reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. Privacy protection in PWS applications model user preferences as hierarchical user profiles. PWS framework called UPS can adaptively generalize profiles by queries while respecting user specified privacy requirements. 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. Two greedy algorithms, namely Greedy dp and Greedy IL, are used for runtime generalization. An online prediction mechanism for deciding whether personalizing a query is beneficial is provided. Extensive experiments demonstrate the effectiveness of the framework. The experimental results also reveal that Greedy IL significantly outperforms Greedy DP in terms of efficiency.
Keywords: Greedy DP and Greedy IL, PWS, UPS, hierarchical profiles.
Title: Supporting Privacy Protection in Personalized Web Search
Author: Mrs .Manisha Deshmukh, Prof. Umesh Kulkarni
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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