Title :
A Bayesian Network Approach to Detecting Privacy Intrusion
Author :
An, Xiangdong ; Jutla, Dawn ; Cercone, Nick
Author_Institution :
Dept. of Finance, Inf. Syst. & Manage. Sci., Saint Mary´´s Univ., Halifax, NS
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
Keywords :
Bayes methods; data mining; data privacy; security of data; Bayesian network approach; database systems; information handling; privacy intrusion detection; private data; Bayesian methods; Cryptography; Data privacy; Database systems; Intelligent agent; Intrusion detection; Operating systems; Protection; Transaction databases; Uncertainty;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2749-3
DOI :
10.1109/WI-IATW.2006.6