Computer Sciences Dept.

Multidimensional K-Anonymity

Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan

K-Anonymity has been proposed as a mechanism for privacy protection in microdata publishing, and numerous recoding “models” have been considered for achieving kanonymity. 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, as well as more specific notions of query answerability. In this paper, we prove that optimal multidimensional anonymization is NP-hard (like previous k-anonymity models). However, we introduce a simple, scalable, greedy algorithm that produces anonymizations that are a constantfactor approximation of optimal. Experimental results show that this greedy algorithm frequently leads to more desirable anonymizations than two optimal exhaustive-search algorithms for single-dimensional models.

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