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Title: Transparent Anonymization: Thwarting Adversaries Who Know the Algorithm

Citation Type: Journal Article

Publication Year: 2010

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Abstract: Numerous generalization techniques have been proposed for privacy-preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data publisher. Consequently, they cannot guard against privacy attacks that exploit various characteristics of the anonymization mechanism. This article provides a practical solution to this problem. First, we propose an analytical model for evaluating disclosure risks, when an adversary knows everything in the anonymization process, except the sensitive values. Based on this model, we develop a privacy principle, transparent l-diversity, which ensures privacy protection against such powerful adversaries. We identify three algorithms that achieve transparent l-diversity, and verify their effectiveness and efficiency through extensive experiments with real data.

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Authors: Xiao, Xiaokui; Koudas, Nick; Tao, Yufei

Periodical (Full): ACM Transactions on Database Systems

Issue: 2

Volume: 35

Pages:

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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