Generalizing data to provide anonymity when disclosing information (abstract)
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The Web is the largest repository of information. Personal information is usually scattered on various pages of different websites. Search engines have made it easier to find personal information. An attacker may collect a user's scattered information together via search engines, and infer some privacy information. We call this kind of privacy attack Privacy Inference Attack via Search Engines. In this paper, we propose a user-side automatic detection service for detecting the privacy leakage before publishing personal information. In the user-side service, we construct a User Information Correlation (UICA) graph to model the association between user information returned by search engines. We map the privacy inference attack into a decision problem of searching a privacy inferring path with the maximal probability in the UICA graph. We propose a Privacy Leakage Detection Probability (PLD-Probability) algorithm to find the privacy inferring path. Extensive experiments indicate that the algorithm is reasonable and effective.