Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
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
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
On privacy preservation against adversarial data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A reconstruction-based algorithm for classification rules hiding
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
The price of privacy and the limits of LP decoding
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Universally utility-maximizing privacy mechanisms
Proceedings of the forty-first annual ACM symposium on Theory of computing
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Relationship privacy: output perturbation for queries with joins
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Computational Differential Privacy
CRYPTO '09 Proceedings of the 29th Annual International Cryptology Conference on Advances in Cryptology
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Proceedings of the 18th ACM conference on Information and knowledge management
Differential privacy with compression
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Towards an axiomatization of statistical privacy and utility
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Private and continual release of statistics
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Towards privacy for social networks: a zero-knowledge based definition of privacy
TCC'11 Proceedings of the 8th conference on Theory of cryptography
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
When random sampling preserves privacy
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
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In this article, we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application. The goal of Pufferfish is to allow experts in an application domain, who frequently do not have expertise in privacy, to develop rigorous privacy definitions for their data sharing needs. In addition to this, the Pufferfish framework can also be used to study existing privacy definitions. We illustrate the benefits with several applications of this privacy framework: we use it to analyze differential privacy and formalize a connection to attackers who believe that the data records are independent; we use it to create a privacy definition called hedging privacy, which can be used to rule out attackers whose prior beliefs are inconsistent with the data; we use the framework to define and study the notion of composition in a broader context than before; we show how to apply the framework to protect unbounded continuous attributes and aggregate information; and we show how to use the framework to rigorously account for prior data releases.