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Atlanta, GA, USA
April 3, 2006 to April 7, 2006
ISBN: 0-7695-2570-9
pp: 25
Kristen LeFevre , University of Wisconsin, Madison
David J. DeWitt , University of Wisconsin, Madison
Raghu Ramakrishnan , University of Wisconsin, Madison
ABSTRACT
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. <p>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.</p>
INDEX TERMS
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CITATION
Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan, "Mondrian Multidimensional K-Anonymity", ICDE, 2006, 22nd International Conference on Data Engineering, 22nd International Conference on Data Engineering 2006, pp. 25, doi:10.1109/ICDE.2006.101
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