Anonymizing user profiles for personalized web search

We study the problem of anonymizing user profiles so that user privacy is sufficiently protected while the anonymized profiles are still effective in enabling personalized web search. We propose a Bayes-optimal privacy notion to bound the prior and posterior probability of associating a user with an individual term in the anonymized user profile set. We also propose a novel bundling technique that clusters user profiles into groups by taking into account the semantic relationships between the terms while satisfying the privacy constraint. We evaluate our approach through a set of preliminary experiments using real data demonstrating its feasibility and effectiveness. Categories and Subject Descriptors: H.2.7 [Database Administration]: Security, integrity, and protection; H.3.3. [Information Search and Retrieval] General Terms: Design, Experimentation, Security

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Yun Zhu, Li Xiong, Christopher Verdery
• Keywords: Internet Technology, Profiles, User, User Profiles, WWW 2010