Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Interval Protection of Confidential Information in a Database
INFORMS Journal on Computing
Protecting privacy in tabular healthcare data: explicit uncertainty for disclosure control
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Secure and useful data sharing
Decision Support Systems
Minimizing Information Loss and Preserving Privacy
Management Science
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data
Information Systems Research
Statistical confidentiality: Optimization techniques to protect tables
Computers and Operations Research
We have met the enemy and he is us
Proceedings of the 2008 workshop on New security paradigms
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Decision Support Systems
On the Prevention of Fraud and Privacy Exposure in Process Information Flow
INFORMS Journal on Computing
Disclosure Control of Confidential Data by Applying Pac Learning Theory
Journal of Database Management
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
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A practical model and an associated method are developed for providing consistent, deterministically correct responses to ad-hoc queries to a database containing a field of binary confidential data. COUNT queries, i.e., the number of selected subjects whose confidential datum is positive, are to be answered. Exact answers may allow users to determine an individual's confidential information. Instead, the proposed technique gives responses in the form of a number plus a guarantee so that the user can determine an interval that is sure to contain the exact answer. At the same time, the method is also able to provide both deterministic and stochastic protection of the confidential data to the subjects of the database. Insider threat is defined precisely and a simple option for defense against it is given. Computational results on a simulated database are very encouraging in that most queries are answered with tight intervals, and that the quality of the responses improves with the number of subjects identified by the query. Thus the results are very appropriate for the very large databases prevalent in business and governmental organizations. The technique is very efficient in terms of both time and storage requirements, and is readily scalable and implementable.