Hiding association rules by using confidence and support

Elena Dasseni, Vassilios S. Verykios, Ahmed Elmagarmid, Elisa Bertino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

169 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages369-383
Number of pages15
Volume2137
ISBN (Print)3540427333, 9783540427339
Publication statusPublished - 2001
Externally publishedYes
Event4th International Information Hiding Workshop, IHW 2001 - Pittsburgh, United States
Duration: 25 Apr 200127 Apr 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2137
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Information Hiding Workshop, IHW 2001
CountryUnited States
CityPittsburgh
Period25/4/0127/4/01

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Dasseni, E., Verykios, V. S., Elmagarmid, A., & Bertino, E. (2001). Hiding association rules by using confidence and support. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2137, pp. 369-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2137). Springer Verlag.