User Customizable Privacy-Preserving Personalized Web Search

Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users' reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. This paper proposes a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user specified privacy requirements. Present an algorithm, namely GreedylL, for runtime generalization. The experimental results show that GreedyIL performs efficiently.