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Quantifying Web-Search Privacy


Web search queries reveal extensive information about users’ personal lives to the search engines and Internet eavesdroppers. Obfuscating search queries through adding dummy queries is a practical and user-centric protection mechanism to hide users’ search intentions and interests. Despite few such obfuscation methods and tools, there is no generic quantitative methodology for evaluating users’ web-search privacy. In this paper, we provide such a methodology. We formalize adversary’s background knowledge and attacks, the users’ privacy objectives, and the algorithms to evaluate effectiveness of query obfuscation mechanisms. We build upon machine-learning algorithms to learn the linkability between user queries...

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