Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Applied Artificial Intelligence
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Security in Databases: A Combinatorial Study
Journal of the ACM (JACM)
Journal of Computer and System Sciences - Special issue on PODS 2000
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
A Bayesian Approach for on-Line Max Auditing
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
A Bayesian approach for on-line max and min auditing
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Reasoning under Uncertainty in On-Line Auditing
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
A Bayesian model for disclosure control in statistical databases
Data & Knowledge Engineering
Causal independence for knowledge acquisition and inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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We consider the problem of auditing databases that support statistical sum/count/max/min queries to protect the privacy of sensitive information. We study the case in which the domain of the sensitive information is the boolean set. Principles and techniques developed for the privacy of statistical databases in the case of continuous attributes do not always apply here. We provide a probabilistic framework for the on-line auditing and we show that sum/count/min/max queries can be audited by means of a Bayesian network.