Anonymizing User Profiles for Personalized Web Search

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Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
The authors 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. They 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. They 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.
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