Association Rule Hiding

Vassilios S. Verykios, Ahmed K. Elmagarmid, Elisa Bertino, Yucel Saygin, Elena Dasseni

Research output: Contribution to journalArticle

333 Citations (Scopus)

Abstract

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that 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, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.

Original languageEnglish
Pages (from-to)434-447
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume16
Issue number4
DOIs
Publication statusPublished - 1 Apr 2004

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Keywords

  • Association rule mining
  • Privacy preserving data mining
  • Sensitive rule hiding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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