A Bayesian Network Approach to Detecting Privacy Intrusion

Authors:
Xiangdong An;Dawn Jutla;Nick Cercone
Affiliations:
Saint Mary's University, Canada/ Dalhousie University, Canada;Saint Mary's University, Canada;Dalhousie University, Canada
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

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Abstract

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.