Title :
Probabilistic Internal Privacy Intrusion Detection
Author :
An, Xiangdong ; Jutla, Dawn ; Cercone, Nick
Author_Institution :
Dept. of Finance, Inf. Syst., & Manage. Sci., Saint Mary´´s Univ., Halifax, NS
Abstract :
Many organizations need to maintain a lot of private data to run their businesses. Private data could be violated by both the inside and the outside intruders. In this paper, we propose a probabilistic method to detect insider privacy intrusion in database systems
Keywords :
belief networks; data privacy; database management systems; statistical distributions; Bayesian networks; database systems; insider privacy intrusion; private data; probabilistic BN internal privacy intrusion detection; probabilistic method; Bayesian methods; Computer science; Data privacy; Database systems; Employee rights; Finance; Frequency; Intrusion detection; Protection; Spatial databases;
Conference_Titel :
Database Engineering and Applications Symposium, 2006. IDEAS '06. 10th International
Conference_Location :
Delhi
Print_ISBN :
0-7695-2577-6
DOI :
10.1109/IDEAS.2006.38
