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Secure Association Rule Sharing

Full Text: pakdd04.pdf PDF

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.

Citation

S. Oliveira, O. Zaiane, Y. Saygin. "Secure Association Rule Sharing". Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Sydney, Australia, (ed: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang), pp 74-85, May 2004.

Keywords: Privacy preserving data mining, Protecting sensitive knowledge, Sharing association rules, Data sanitization, Sanitizing algorithms
Category: In Conference
Web Links: Springer

BibTeX

@incollection{Oliveira+al:PAKDD04,
  author = {Stanley R. Oliveira and Osmar R. Zaiane and Yücel Saygin},
  title = {Secure Association Rule Sharing},
  Editor = {Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang},
  Pages = {74-85},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining
    (PAKDD)},
  year = 2004,
}

Last Updated: September 10, 2020
Submitted by Sabina P

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