Abstract
Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on l-diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microdata with multiple SAs that may be multi-valued. This paper explores the issue of attribute disclosure in such scenarios. We propose a method called CODIP (Complete Disjoint Projections) that outlines a general solution to deal with the shortcomings in a naïve approach. We also introduce two measures, Association Loss Ratio and Information Exposure Ratio, to quantify data quality and privacy, respectively. We further propose a heuristic CODIP* for CODIP, which obtains a good trade-off in data quality and privacy. Finally, initial experiments show that CODIP* is practically useful on varying numbers of SAs.
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Fang, Y., Ashrafi, M.Z., Ng, S.K. (2011). Privacy beyond Single Sensitive Attribute. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23088-2_13
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DOI: https://doi.org/10.1007/978-3-642-23088-2_13
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