Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Communications of the ACM
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering frequent patterns in sensitive data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Differentially private data cubes: optimizing noise sources and consistency
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Personal privacy vs population privacy: learning to attack anonymization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
How much is enough? choosing ε for differential privacy
ISC'11 Proceedings of the 14th international conference on Information security
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Membership privacy: a unifying framework for privacy definitions
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
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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 give 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.