Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection

Fred Glover (), Lawrence Cox (), Rahul Patil () and James Kelly ()

Annals of Operations Research, 2011, vol. 183, issue 1, 47-73

Abstract: A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentiality of critical components of this data. The challenge is to organize and disseminate data in a form that prevents such critical components from being inferred by groups bent on corporate espionage, to gain competitive advantages, or having a desire to penetrate the security of the information underlying the data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables. We develop stratified ordered (s-ordered) heuristics and a new meta-heuristic learning approach for solving this model, and compare their performance to previous heuristics and to an exact algorithm embodied in the state-of-the-art ILOG- CPLEX software. Our new approaches are based on partitioning the problem into its discrete and continuous components, first creating an s-ordered heuristic that reduces the number of binary variables through a grouping procedure that combines an exact mathematical programming model with constructive heuristics. To gain further advantages we then replace the mathematical programming model with an evolutionary scatter search approach that makes it possible to extend the method to large problems with over 9000 entries. Finally, we introduce a new metaheuristic learning method that significantly improves the quality of solutions obtained. Copyright Springer Science+Business Media, LLC 2011

Keywords: Confidentiality; Mixed integer optimization; Metaheuristics; Adaptive learning; Mathematical programming; Evolutionary computation (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s10479-009-0574-8

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