Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Data publishing against realistic adversaries
Proceedings of the VLDB Endowment
An ad omnia approach to defining and achieving private data analysis
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Towards an axiomatization of statistical privacy and utility
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Towards privacy for social networks: a zero-knowledge based definition of privacy
TCC'11 Proceedings of the 8th conference on Theory of cryptography
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Personal privacy vs population privacy: learning to attack anonymization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
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We introduce a novel privacy framework that we call Membership Privacy. The framework includes positive membership privacy, which prevents the adversary from significantly increasing its ability to conclude that an entity is in the input dataset, and negative membership privacy, which prevents leaking of non-membership. These notions are parameterized by a family of distributions that captures the adversary's prior knowledge. The power and flexibility of the proposed framework lies in the ability to choose different distribution families to instantiate membership privacy. Many privacy notions in the literature are equivalent to membership privacy with interesting distribution families, including differential privacy, differential identifiability, and differential privacy under sampling. Casting these notions into the framework leads to deeper understanding of the strengthes and weaknesses of these notions, as well as their relationships to each other. The framework also provides a principled approach to developing new privacy notions under which better utility can be achieved than what is possible under differential privacy.