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A Bayesian Network Approach to Detecting Privacy Intrusion
Hong Kong, China December 18-December 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI-IATW.2006.62006 IEEE/WIC/ACM International Confe ...
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Xiangdong An, Saint Mary's University, Canada; Dalhousie University, Canada
Dawn Jutla, Saint Mary's University, Canada
Nick Cercone, Dalhousie University, Canada
Personal information privacy could be compromised during information collection, transmission, and handling. In information handling, privacy could be violated by both the inside and the outside intruders. Though, within an organization, private data are generally protected by the organization?s privacy policies and the corresponding platforms for privacy practices, private data could still be misused intentionally or unintentionally by individuals who have legitimate access to them in the organization. In this paper, we propose a Bayesian network-based method for insider privacy intrusion detection in database systems.
Citation:
Xiangdong An, Dawn Jutla, Nick Cercone, "A Bayesian Network Approach to Detecting Privacy Intrusion," wi-iatw, pp.73-76, 2006 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, 2006
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