On Robust and Effective K-Anonymity in Large Databases

  • Wen Jin
  • Rong Ge
  • Weining Qian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


The challenge of privacy-preserving data mining lies in respecting privacy requirements while discovering the original interesting patterns or structures. Existing methods loose the correlations among attributes by transforming the different attributes independently, or cannot guarantee the minimum abstraction level required by legal policies. In this paper, we propose a novel privacy-preserving transformation framework for distance-based mining operations based on the concept of privacy-preserving MicroClusters that satisfy a privacy constraint as well as a significance constraint. Our framework well extends the robustness of the state-of-the-art k-anonymity model by introducing a privacy constraint (minimum radius) while keeping its effectiveness by a significance constraint (minimum number of corresponding data records). The privacy-preserving MicroClusters are made public for data mining purposes, but the original data records are kept private. We present efficient methods for generating and maintaining privacy-preserving MicroClusters and show that data mining operations such as clustering can easily be adapted to the public data represented by MicroClusters instead of the private data records. The experiment demonstrates that the proposed methods achieve accurate clusterings results while preserving the privacy.


Minimum Radius Split Operation Range Constraint Privacy Constraint Privacy Preserve Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wen Jin
    • 1
  • Rong Ge
    • 1
  • Weining Qian
    • 2
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada
  2. 2.Department of Computer ScienceFudan UniversityChina

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