Noise Injection for Search Privacy Protection

Abstract—Extensive work has been devoted to private information retrieval and privacy preserving data mining. To protect user privacy from search engines, however, most current approaches require a server-side deployment and users have little control over their data and privacy. This paper proposes a user-side solution, which models the search privacy threat as an information inference problem and injects noise into user queries to minimize privacy breaches. The search privacy breach is measured as the mutual information between the real user queries and the diluted queries seen by search engines. We give the lower bound for the amount of noise queries required by a perfect privacy protection and provide the optimal protection given the number of noise queries. We verify our results with a special case where the number of noise queries is equal to the number of user queries. The simulation result shows that the noise given by our approach greatly reduces privacy breaches and outperfo...

• Type: Conference
• Year: 2009
• Where: CSE
• Authors: Shaozhi Ye, Shyhtsun Felix Wu, Raju Pandey, Hao Chen
• Keywords: Computer Science And Engineering, CSE 2009, Privacy, Search Privacy, User Queries