Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
ACM Transactions on Database Systems (TODS)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
The tracker: a threat to statistical database security
ACM Transactions on Database Systems (TODS)
Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
Security in Databases: A Combinatorial Study
Journal of the ACM (JACM)
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Practical privacy: the SuLQ framework
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)
Towards robustness in query auditing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
A bayesian approach for on-line sum/count/max/min auditing on boolean data
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Disclosure Control of Confidential Data by Applying Pac Learning Theory
Journal of Database Management
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The paper proposes a novel approach for on-line max and min query auditing, in which a Bayesian network addresses disclosures based on probabilistic inferences that can be drawn from released data. In the literature, on-line max and min auditing has been addressed with some restrictive assumptions, primarily that sensitive values must be all distinct and the sensitive field has a uniform distribution. We remove these limitations and propose a model able to: provide a graphical representation of user knowledge; deal with the implicit delivery of information that derives from denying the answer to a query; and capture user background knowledge. Finally, we discuss the results of experiments aimed at assessing the scalability of the approach, in terms of response time and size of the conditional probability table, and the usefulness of the auditor system, in terms of probability to deny.