2012 Symposium Posters

Project Members
Jaewoo Lee and Chris Clifton

A key challenge in privacy-preserving data mining is ensuring that a data mining result does not inherently violate privacy. ϵ-Differential Privacy appears to provide a solution to this problem. However, there are no clear guidelines on how to set ϵ to satisfy a privacy policy. We given an alternate formulation, Differential Identifiability, parameterized by the probability of individual identification. This provides the strong privacy guarantees of differential privacy, while letting policy makers set parameters based on the established privacy concept of individual identifiability.