Towards a More Reasonable Generalization Cost Metric for K-Anonymization

  • Zude Li
  • Guoqiang Zhan
  • Xiaojun Ye
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4042)


A k-anonymity model contains an anonymity cost metric mechanism, which is critical for the whole k-anonymization process. The existing metrics cannot sufficiently identify the real cost on tabular microdata anonymization. We define a new cost metric that can be used for k-anonymization with the data generalization approach. The metric is more reasonable than the existing ones as it considers generalization range and range ratio rather than generalization height or height ratio, and the contribution of an attribute to the whole tuple rather than the amount of suppression cells. It can be used in most k-anonymity models for computing more precise anonymity costs.


Privacy Protection Sensitive Attribute Preference Criterion Cost Metrics Reasonable Generalization 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zude Li
    • 1
  • Guoqiang Zhan
    • 1
  • Xiaojun Ye
    • 1
  1. 1.Institute of Information System and Engineering School of SoftwareTsinghua UniversityBeijingChina

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