Abstract
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.
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Wang, H.(., Liu, R. (2010). Privacy-Preserving Publishing Data with Full Functional Dependencies. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12098-5_14
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DOI: https://doi.org/10.1007/978-3-642-12098-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12097-8
Online ISBN: 978-3-642-12098-5
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