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)
ACM Transactions on Database Systems (TODS)
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
A model of statistical database their security
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
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Security in Databases: A Combinatorial Study
Journal of the ACM (JACM)
Auditing Interval-Based Inference
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
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Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Probabilistic encryption & how to play mental poker keeping secret all partial information
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Auditing for secure statistical databases
ACM '81 Proceedings of the ACM '81 conference
Journal of Computer and System Sciences - Special issue on PODS 2000
Logconcave Functions: Geometry and Efficient Sampling Algorithms
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Towards robustness in query auditing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A formal analysis of information disclosure in data exchange
Journal of Computer and System Sciences
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
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Imagine a data set consisting of private information about individuals. The online query auditing problem is: given a sequence of queries that have already been posed about the data, their corresponding answers and given a new query, deny the answer if privacy can be breached or give the true answer otherwise. We investigate the fundamental problem that query denials leak information. This problem was largely overlooked in previous work on auditing. Because of this oversight, some of the previously suggested auditors can be used by an attacker to compromise the privacy of a large fraction of the individuals in the data. To overcome this problem, we introduce a new model called simulatable auditing where query denials provably do not leak information. We present a simulatable auditing algorithm for max queries under the classical definition of privacy where a breach occurs if a sensitive value is fully compromised. Because of the known limitations of the classical definition of compromise, we describe a probabilistic notion of (partial) compromise, closely related to the notion of semantic security. We demonstrate that sum queries can be audited in a simulatable fashion under probabilistic compromise, making some distributional assumptions.