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Transparent Anonymization: Thwarting Adversaries Who Know the Algorithm

By Xiaokui Xiao, Yufei Tao and Nick Koudas

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

Topics: General Terms, Theory, Algorithms, Experimentation Additional Key Words and Phrases, Privacy-preserving data publishing, generalization, l-diversity ACM Reference Format
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.187.4592
Provided by: CiteSeerX
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