Privacy intrusion detection using dynamic Bayesian networks
Xiangdong An, Dawn Jutla, Nick J. Cercone · 2006
Concerns for personal information privacy could be produced during information collection, transmission and handling. In information handling, privacy could be compromised from both inside and outside of organizations. Within an organization, private data are generally protected by organizations' privacy policies and the corresponding platforms for privacy practices. However, private data could still be misused intentionally or unintentionally by individuals who have legitimate accesses to them. In general, activities of a database operator form a stochastic process, and at different time, privacy intrusion behavior may show different features. In particular, one's past activities can help determine the natures of his/her current practices. In this paper, we propose to use dynamic Bayesian networks to model such temporal environments and detect any privacy intrusions happened within them.