Mondrian Multidimensional K-Anonymity

  • Authors:
  • Kristen LeFevre;David J. DeWitt;Raghu Ramakrishnan

  • Affiliations:
  • University of Wisconsin, Madison;University of Wisconsin, Madison;University of Wisconsin, Madison

  • Venue:
  • ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
  • Year:
  • 2006

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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. 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.