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Title: ASAP: Eliminating Algorithm-based Disclosure in Privacy-Preserving Data Publishing

Citation Type: Journal Article

Publication Year: 2011

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Abstract: Numerous privacy-preserving data publishing algorithms were proposed to achieve privacy guarantees such as l-diversity. Many of them, however, were recently found to be vulnerable to algorithm-based disclosurei.e., privacy leakage incurred by an adversary who is aware of the privacy-preserving algorithm being used. This paper describes generic techniques for correcting the design of existing privacy-preserving data publishing algorithms to eliminate algorithm-based disclosure. We first show that algorithm-based disclosure is more prevalent and serious than previously studied. Then, we strictly define Algorithm-SAfe Publishing (ASAP) to capture and eliminate threats from algorithm-based disclosure. To correct the problems of existing data publishing algorithms, we propose two generic tools to be integrated in their design: global look-ahead and local look-ahead. To enhance data utility, we propose another generic tool called stratified pick-up. We demonstrate the effectiveness of our tools by applying them to several popular l-diversity algorithms: Mondrian, Hilb, and MASK. We conduct extensive experiments to demonstrate the effectiveness of our tools in terms of data utility and efficiency.

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Authors: Jin, Xin; Zhang, Nan; Das, Gautam

Periodical (Full): Information Systems

Issue: 5

Volume: 36

Pages: 859-880

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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