Optimal distribution of restricted ranges in secure statistical databases
One of the goals of statistical databases is to provide statistics about groups of individuals while protecting their privacy. Sometimes, by correlating enough statistics, sensitive data about individual can be inferred. The problem of protecting against such indirect disclosures of confidential data is called the inference problem and a protecting mechanism—an inference control. A good inference control mechanism should be effective (it should provide security to a reasonable extent) and feasible (a practical way exists to enforce it). At the same time it should retain the richness of the information revealed to the users. During the last few years several techniques were developed for controlling inferences. One of the earliest inference controls for statistical databases restricts the responses computed over too small or too large query-sets. However, this technique is easily subverted. Recently some results were presented (see [Michalewicz & Chen, 1989]) for measuring the usability and security of statistical databases for different distributions of frequencies of statistical queries, based on the concept of multiranges. In this paper we use the genetic algorithm approach to maximize the usability of a statistical database, at the same time providing a reasonable level of security. We discuss also the importance of this new technique.
KeywordsGenetic Algorithm Genetic Operator Restricted Range Solution Vector Statistical Database
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- [Davis, 1987]Davis, L., (editor), Genetic Algorithms and Simulated Annealing, Pitman, London, 1987.Google Scholar
- [Denning et al., 1979]Denning, D.E., Denning, P.J. and Schwartz, M.D., The Tracker: A Threat to Statistical Database Security, ACMToDS, Vol.4, No.1, March 1979, pp.76–96.Google Scholar
- [Denning & Schlörer, 1980]Denning, D.E. and Schlörer, J., A Fast Procedure for Finding a Tracker in a Statistical Database, ACMToDS, Vol.5, No.1, March 1980, pp.88–102.Google Scholar
- [Denning & Schlörer, 1983]Denning, D.E. and Schlörer, J., Inference Controls for Statistical Databases, Computer, Vol.16, No.7, July 1983, pp.69–85Google Scholar
- [Holland, 1959]Holland, J., A Universal Computer Capable of Executing an Arbitrary Number of Sub-Programs Simultaneously, Proc. 1959 E.J.C.C., pp.108–113.Google Scholar
- [Holland, 1975]Holland, J., Adaptation in Natural and Artificial Systems, Ann Arbor: University of Michigan Press, 1975.Google Scholar
- [Michalewicz, 1981]Michalewicz, Z., Compromisability of a Statistical Database, Information Systems, Vol.6, No.4, Dec. 1981, pp.301–304.Google Scholar
- [Michalewicz & Yeo, 1987]Michalewicz, Z. and Yeo, A., Multiranges and Multitrackers in Statistical Databases, Fundamenta Informaticae, Vol. X, No.4, Dec. 1987, pp.41–48.Google Scholar
- [Michalewicz & Chen, 1988]Michalewicz, Z. and Chen, K.-W., Ranges and Trackers in Statistical Databases, Proc. of the 4-th International Conference on Statistical and Scientific Databases, Rome, 21–23 June 1988, and Springer-Verlag Lecture Notes in Computer Science, No.339, pp.193–206Google Scholar
- [Michalewicz & Chen, 1989]Michalewicz, Z. and Chen, K.-W., Usability and Security of Statistical Databases, submitted to the Australian Computer Journal.Google Scholar
- [Schlörer, 1980]Schlörer, J., Disclosure from Statistical Databases: Quantitative Aspects of Trackers, ACMToDS, Vol.5, No.4, Dec. 1980, pp.467–492.Google Scholar
- [Von Neumann, 1955]Von Neumann, J., Theory of Self-Reproducing Automata, edited by Burks, University of Illinois Press, 1966.Google Scholar