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ABSTRACT
This paper considers the problem of providing security to statistical databases against disclosure of confidential information. Security-control methods suggested in the literature are classified into four general approaches: conceptual, query restriction, data perturbation, and output perturbation.
Criteria for evaluating the performance of the various security-control methods are identified. Security-control methods that are based on each of the four approaches are discussed, together with their performance with respect to the identified evaluation criteria. A detailed comparative analysis of the most promising methods for protecting dynamic-online statistical databases is also presented.
To date no single security-control method prevents both exact and partial disclosures. There are, however, a few perturbation-based methods that prevent exact disclosure and enable the database administrator to exercise "statistical disclosure control." Some of these methods, however introduce bias into query responses or suffer from the 0/1 query-set-size problem (i.e., partial disclosure is possible in case of null query set or a query set of size 1).
We recommend directing future research efforts toward developing new methods that prevent exact disclosure and provide statistical-disclosure control, while at the same time do not suffer from the bias problem and the 0/1 query-set-size problem. Furthermore, efforts directed toward developing a bias-correction mechanism and solving the general problem of small query-set-size would help salvage a few of the current perturbation-based methods.
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CITED BY 135
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Ran Canetti , Yuval Ishai , Ravi Kumar , Michael K. Reiter , Ronitt Rubinfeld , Rebecca N. Wright, Selective private function evaluation with applications to private statistics, Proceedings of the twentieth annual ACM symposium on Principles of distributed computing, p.293-304, August 2001, Newport, Rhode Island, United States
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Jon Kleinberg , Christos Papadimitriou , Prabhakar Raghavan, Auditing Boolean attributes, Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.86-91, May 15-18, 2000, Dallas, Texas, United States
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F. M. Malvestuto , M. Moscarini , M. Rafanelli, Suppressing marginal cells to protect sensitive information in a two-dimensional statistical table (extended abstract), Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, p.252-258, May 29-31, 1991, Denver, Colorado, United States
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REVIEW
"Mary McLeish : Reviewer"
A statistical database (SDB) is any traditional database system in
which queries are restricted to statistical aggregates (such as sample
mean and count); an example is the US Census Bureau database. It is
often required that the system be sec
more...
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