Mondrian Multidimensional K-Anonymity

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Mondrian Multidimensional K-Anonymity
K-Anonymity has been proposed as a mechanism for protecting privacy 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 and more specific notions of query answerability. Optimal multidimensional anonymization is NP-hard (like previous optimal k-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.
Kristen LeFevre, David J. DeWitt, Raghu Ramakrishn
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2006
Where ICDE
Authors Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan
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