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Web search logs contain extremely sensitive data, as evidenced by the recentAOL incident. However, storing and analyzing search logs can be very useful formany purposes (i.e. investigating human behavior). Thus, an important researchquestion is how to privately sanitize search logs. Several search loganonymization techniques have been proposed with concrete privacy models.However, in all of these solutions, the output utility of the techniques isonly evaluated rather than being maximized in any fashion. Indeed, foreffective search log anonymization, it is desirable to derive the optimal(maximum utility) output while meeting the privacy standard. In this paper, wepropose utility-maximizing sanitization based on the rigorous privacy standardof differential privacy, in the context of search logs. Specifically, weutilize optimization models to maximize the output utility of the sanitizationfor different applications, while ensuring that the production processsatisfies differential privacy. An added benefit is that our novelrandomization strategy ensures that the schema of the output is identical tothat of the input. A comprehensive evaluation on real search logs validates theapproach and demonstrates its robustness and scalability.

Differentially Private Search Log Sanitization with Optimal Output Utility