A theoretical model for obfuscating web navigation trails
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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The text search queries in an enterprise can reveal the users' topic of interest, and in turn confidential staff or business information. To safeguard the enterprise from consequences arising from a disclosure of the query traces, it is desirable to obfuscate the true user intention from the search engine, without requiring it to be re-engineered. In this paper, we advocate a unique approach to profile the topics that are relevant to the user intention. Based on this approach, we introduce an $(\epsilon_1, \epsilon_2)$-privacy model that allows a user to stipulate that topics relevant to her intention at $\epsilon_1$ level should appear to any adversary to be innocuous at $\epsilon_2$ level. We then present a Top Priv algorithm to achieve the customized $(\epsilon_1, \epsilon_2)$-privacy requirement of individual users through injecting automatically formulated fake queries. The advantages of Top Priv over existing techniques are confirmed through benchmark queries on a real corpus, with experiment settings fashioned after an enterprise search application.