Privacy preserving association rule mining

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Author

Y. Saygin, V.S. Verykios, A.K. Elmagarmid

Entry type

proceedings

Abstract

The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms

Date

2002

URL

http://ieeexplore.ieee.org/xpl ... .jsp?&arnumber=995109

Booktitle

Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, 2002. RIDE-2EC 2002. Proceedings. Twelfth International Workshop on

Key alpha

Elmagarmid

Pages

151-158

Affiliation

Purdue University

Publication Date

2002-00-00

Isbn

0-7695-1480-4

BibTex-formatted data

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@Proceedings{ Elmagarmid,
	title = "Privacy preserving association rule mining",
	author = "Y. Saygin, V.S. Verykios, A.K. Elmagarmid",
	year = "2002",
	booktitle = "Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems, 2002. RIDE-2EC 2002. Proceedings. Twelfth International Workshop on",
	pages = "151-158",
	abstract = "The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms",
	affiliation = "Purdue University",
	isbn = "0-7695-1480-4",
	url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?&arnumber=995109",
}