Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1151454.1151493 | ICEC |
Keywords | Field | DocType |
dynamic bayesian network,information privacy,intrusion detection,probabilistic reasoning,stochastic process,dynamic bayesian networks,privacy policy | Internet privacy,Privacy by Design,Computer science,Computer security,Privacy policy,Personally identifiable information,Probabilistic logic,Information privacy,Intrusion detection system,Privacy software,Dynamic Bayesian network | Conference |
ISBN | Citations | PageRank |
1-59593-392-1 | 16 | 0.79 |
References | Authors | |
34 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangdong An | 1 | 64 | 13.56 |
| Dawn N. Jutla | 2 | 256 | 43.33 |
| Nick Cercone | 3 | 1999 | 570.62 |