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22nd International Conference on Data Engineering (ICDE'06)
Mondrian Multidimensional K-Anonymity
Atlanta, Georgia
April 03-April 07
ISBN: 0-7695-2570-9
Kristen LeFevre, University of Wisconsin, Madison
David J. DeWitt, University of Wisconsin, Madison
Raghu Ramakrishnan, University of Wisconsin, Madison
K-Anonymity has been proposed as a mechanism for protecting privacy in microdata publishing, and numerous recoding "models" have been considered for achieving anonymity. This paper proposes a new multidimensional model, which provides an additional degree of flexibility not seen in previous (single-dimensional) approaches. Often this flexibility leads to higher-quality anonymizations, as measured both by general-purpose metrics and more specific notions of query answerability.

Optimal multidimensional anonymization is NP-hard (like previous optimal anonymity problems). However, we introduce a simple greedy approximation algorithm, and experimental results show that this greedy algorithm frequently leads to more desirable anonymizations than exhaustive optimal algorithms for two single-dimensional models.

Citation:
Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan, "Mondrian Multidimensional K-Anonymity," icde, pp.25, 22nd International Conference on Data Engineering (ICDE'06), 2006
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