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Hiding Association Rules by Using Confidence and Support

  • Elena Dasseni
  • Vassilios S. Verykios
  • Ahmed K. Elmagarmid
  • Elisa Bertino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2137)

Abstract

Large repositories of data contain sensitive information which must be protected against unauthorized access. Recent advances, in data mining and machine learning algorithms, have increased the disclosure risks one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. In this paper, we investigate confidentiality issues of a broad category of rules, which are called association rules. If the disclosure risk of some of these rules are above a certain privacy threshold, those rules must be characterized as sensitive. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferencing sensitive data, or they may provide business competitors with an advantage.

Keywords

Association Rule Association Rule Mining Data Mining Technique Source Database Disclosure Risk 
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 2001

Authors and Affiliations

  • Elena Dasseni
    • 1
  • Vassilios S. Verykios
    • 2
  • Ahmed K. Elmagarmid
    • 3
  • Elisa Bertino
    • 1
  1. 1.Dipartimento di Scienze dell’InformazioneUniversita’ di MilanoMilanoItaly
  2. 2.College of Information Science and TechnologyDrexel UniversityUSA
  3. 3.Department of Computer SciencesPurdue UniversityUSA

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