ADKAM: A-Diversity K-Anonymity Model via Microaggregation

  • Liang Cheng
  • Shaoyin Cheng
  • Fan Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9065)


A great challenge in privacy preservation is to trade off two important issues: data utility and privacy preservation, in publication of dataset which usually contains sensitive information. Anonymization is a well-represent approach to achieve this, and there exist several anonymity models. Most of those models mainly focuses on protecting privacy exerting identical protection for the whole table with pre-defined parameters. As a result, it could not meet the diverse requirements of protection degrees varied with different sensitive values.Motivated by this, this paper firstly introduces an a-diversity k-anonymity model (ADKAM) to satisfy the diversity deassociation for sensitive values, ant then designs a framework based on an improved microaggregation algorithm, as an alternative to generalization/ suppression to achieve anonymization. By using this framework, we improve the data utility and disclosure risk of privacy disclosure. We conduct several experiments to validate our schemes.


Data publishing k-anonymity privacy preservation microaggregation 


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Liang Cheng
    • 1
  • Shaoyin Cheng
    • 1
  • Fan Jiang
    • 1
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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