Paper Info
Title
Differential identifiability
Abstract
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.
Year
DOI
Venue
2012
10.1145/2339530.2339695
KDD
Keywords
DocType
Citations 
privacy-preserving data mining,data mining result,established privacy concept,policy maker,differential identifiability,individual identifiability,strong privacy guarantee,individual identification,differential privacy,privacy policy
Conference
11
PageRank 
References 
Authors
0.64
14
2
Name
Order
Citations
PageRank
Jaewoo Lee130835.20
Chris Clifton23327544.44