Publication details
On the role of contextual information for privacy attacks and classification
| Authors | |
|---|---|
| Year of publication | 2004 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2004 IEEE International Conference on Data Mining, Workshop on Privacy and Security Aspects of Data Mining |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | Contextual Information; Privacy; Attacks; Classification |
| Description | Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This lead us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability -- the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling -- and linking -- users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets. |