Secure association rule sharing

Oliveira, Stanley R. M. and Zaïane, Osmar R. and Saygın, Yücel (2004) Secure association rule sharing. Lecture Notes in Computer Science (Advances in Knowledge Discovery and Data Mining), 3056 . pp. 74-85. ISSN 0302-9743 (Print) 1611-3349 (Online)

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Official URL: http://dx.doi.org/10.1007/b97861


The sharing of association rules is often beneficial in industry, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns which we call restrictive rules. These restrictive rules must be protected before sharing since they are paramount for strategic decisions and need to remain private. To address this challenging problem, we propose a unified framework for protecting sensitive knowledge before sharing. This framework encompasses: (a) an algorithm that sanitizes restrictive rules, while blocking some inference channels. We validate our algorithm against real and synthetic datasets; (b) a set of metrics to evaluate attacks against sensitive knowledge and the impact of the sanitization. We also introduce a taxonomy of sanitizing algorithms and a taxonomy of attacks against sensitive knowledge.

Item Type:Article
Uncontrolled Keywords:privacy preserving data mining; protecting sensitive knowledge; sharing association rules; data sanitization; sanitizing algorithms
Subjects:Q Science > QA Mathematics > QA075 Electronic computers. Computer science
ID Code:372
Deposited By:Yücel Saygın
Deposited On:17 Feb 2007 02:00
Last Modified:15 Feb 2010 15:26

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