Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards robustness in query auditing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Query relaxation using malleable schemas
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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 2009 ACM SIGMOD International Conference on Management of data
Private record matching using differential privacy
Proceedings of the 13th International Conference on Extending Database Technology
Secure personal data servers: a vision paper
Proceedings of the VLDB Endowment
Mixture of gaussian models and bayes error under differential privacy
Proceedings of the first ACM conference on Data and application security and privacy
The application of differential privacy to health data
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Differential privacy data release through adding noise on average value
NSS'12 Proceedings of the 6th international conference on Network and System Security
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Given a dataset containing sensitive personal information, a statistical database answers aggregate queries in a manner that preserves individual privacy. We consider the problem of constructing a statistical database using output perturbation, which protects privacy by injecting a small noise into each query result. We show that the state-of-the-art approach, ε-differential privacy, suffers from two severe deficiencies: it (i) incurs prohibitive computation overhead, and (ii) can answer only a limited number of queries, after which the statistical database has to be shut down. To remedy the problem, we develop a new technique that enforces ε-different privacy with economical cost. Our technique also incorporates a query relaxation mechanism, which removes the restriction on the number of permissible queries. The effectiveness and efficiency of our solution are verified through experiments with real data.