k-Anonymous Patterns

  • Maurizio Atzori
  • Francesco Bonchi
  • Fosca Giannotti
  • Dino Pedreschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)


It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.


Association Rule Frequent Itemsets Association Rule Mining Support Threshold Minimum Support Threshold 
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 2005

Authors and Affiliations

  • Maurizio Atzori
    • 1
    • 2
  • Francesco Bonchi
    • 2
  • Fosca Giannotti
    • 2
  • Dino Pedreschi
    • 1
  1. 1.Pisa KDD Laboratory, Computer Science DepartmentUniversity of PisaItaly
  2. 2.Pisa KDD Laboratory, ISTI – CNRPisaItaly

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