We study the privacy threat by publishing data that contains full functional dependencies (FFDs). We 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, we formalize the FFD-based privacy attack, define the privacy model (d, ℓ)-inference to combat the FFD-based attack, and design robust anonymization algorithm that achieves (d, ℓ)-inference. The efficiency and effectiveness of our approach are demonstrated by the empirical study. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Wang, H., & Liu, R. (2010). Privacy-preserving publishing data with full functional dependencies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5982 LNCS, pp. 176–183). https://doi.org/10.1007/978-3-642-12098-5_14
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