Hiding Association Rules by Using Confidence and Support

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Author

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

Entry type

article

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. Portions of this work were supported by sponsors of the Center for Education and Research in Information Assurance and Security.

Date

2001

URL

http://www.springerlink.com/content/26a5l6nedt49bpfp/

Booktitle

Information Hiding

Key alpha

Elmagarmid

Pages

369-383

Publisher

Springer Berlin / Heidelberg

Series

Lecture Notes in Computer Science

Volume

2137

Affiliation

Purdue University

Publication Date

2001-00-00

Copyright

2001

Isbn

978-3-540-42733-9

BibTex-formatted data

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@Article{ Elmagarmid,
	title = " Hiding Association Rules by Using Confidence and Support",
	author = "Elena Dasseni, Vassilios S. Verykios, Ahmed K. Elmagarmid, Elisa Bertino",
	year = "2001",
	booktitle = "Information Hiding",
	pages = "369-383",
	publisher = "Springer Berlin / Heidelberg",
	series = "Lecture Notes in Computer Science",
	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.
Portions of this work were supported by sponsors of the Center for Education and Research in Information Assurance and Security.",
	affiliation = "Purdue University",
	copyright = "2001",
	isbn = "978-3-540-42733-9",
	url = "http://www.springerlink.com/content/26a5l6nedt49bpfp/",
}