Partial cell suppression: A new methodology for statistical disclosure control
Statistics and Computing
Extending Cell Suppression to Protect Tabular Data against Several Attackers
Inference Control in Statistical Databases, From Theory to Practice
Sanitization models and their limitations
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Cell suppression problem: A genetic-based approach
Computers and Operations Research
Statistical confidentiality: Optimization techniques to protect tables
Computers and Operations Research
Disclosure Analysis and Control in Statistical Databases
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
An efficient online auditing approach to limit private data disclosure
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A genetic approach to statistical disclosure control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Branch-and-cut versus cut-and-branch algorithms for cell suppression
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Automatic structure detection in constraints of tabular data
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Disclosure analysis for two-way contingency tables
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
Information Sciences: an International Journal
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Cell suppression is a widely used technique for protecting sensitive information in statistical data presented in tabular form. Previous works on the subject mainly concentrate on 2- and 3-dimensional tables whose entries are subject to marginal totals. In this paper we address the problem of protecting sensitive data in a statistical table whose entries are linked by a generic system of linear constraints. This very general setting covers, among others,k-dimensional tables with marginals as well as the so-calledhierarchical andlinked tables that are very often used nowadays for disseminating statistical data. In particular, we address the optimization problem known in the literature as the (secondary) Cell Suppression Problem, in which the information loss due to suppression has to be minimized. We introduce a new integer linear programming model and outline an enumerative algorithm for its exact solution. The algorithm can also be used as a heuristic procedure to find near-optimal solutions. Extensive computational results on a test-bed of 1,160 real-world and randomly generated instances are presented, showing the effectiveness of the approach. In particular, we were able to solve to proven optimality 4-dimensional tables with marginals as well as linked tables of reasonable size (to our knowledge, tables of this kind were never solved optimally by previous authors).