Privacy-Preserving Publishing Data with Full Functional Dependencies

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Provided by: Stevens Creek Software LLC
Topic: Security
Format: PDF
The authors study the privacy threat by publishing data that contains Full Functional Dependencies (FFDs). They show that the cross-attribute correlations by FFDs can bring potential vulnerability to privacy. Unfortunately, none of the existing anonymization principles can effectively prevent against the FFD-based privacy attack. In this paper, they formalize the FFD-based privacy attack, define the privacy model (d, a)-inference to combat the FFD-based attack, and design robust anonymization algorithm that achieves (d, a)-inference. The efficiency and effectiveness of their approach are demonstrated by the empirical study.
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