Full Citation

Title: Privacy-Preserving Publishing Micordata with Full Functional Dependencies

Citation Type: Journal Article

Publication Year: 2011

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Abstract: Data publishing has generated much concern on individual privacy. Recent work has shownthat different background knowledge can bring various threats to the privacy of published data.In this paper, we study the privacy threat from the full functional dependency (FFD) that isused as part of adversary knowledge. We show that the cross-attribute correlations by FFDs(e.g., Phone?Zipcode) can bring potential vulnerability. Unfortunately, none of the existinganonymization principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent againstan FFD-based privacy attack. We formalize the FFD-based privacy attack and define the privacymodel, d;l-inference, to combat the FD-based attack. We distinguish the safe FFDs that willnot jeopardize privacy from the unsafe ones. We design robust algorithms that can efficientlyanonymize the microdata with low information loss when the unsafe FFDs are present. Theefficiency and effectiveness of our approach are demonstrated by the empirical study.

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Authors: Wang, Hui; Liu, Ruilin

Periodical (Full): Data & Knowledge Engineering

Issue:

Volume: 70

Pages: 249-268

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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