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Recent advances in optimization techniques for statistical tabular data protection

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  • Castro, Jordi

Abstract

One of the main services of National Statistical Agencies (NSAs) for the current Information Society is the dissemination of large amounts of tabular data, which is obtained from microdata by crossing one or more categorical variables. NSAs must guarantee that no confidential individual information can be obtained from the released tabular data. Several statistical disclosure control methods are available for this purpose. These methods result in large linear, mixed integer linear, or quadratic mixed integer linear optimization problems. This paper reviews some of the existing approaches, with an emphasis on two of them: cell suppression problem (CSP) and controlled tabular adjustment (CTA). CSP and CTA have concentrated most of the recent research in the tabular data protection field. The particular focus of this work is on methods and results of practical interest for end-users (mostly, NSAs). Therefore, in addition to the resulting optimization models and solution approaches, computational results comparing the main optimization techniques – both optimal and heuristic – using real-world instances are also presented.

Suggested Citation

  • Castro, Jordi, 2012. "Recent advances in optimization techniques for statistical tabular data protection," European Journal of Operational Research, Elsevier, vol. 216(2), pages 257-269.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:2:p:257-269
    DOI: 10.1016/j.ejor.2011.03.050
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    References listed on IDEAS

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    1. Jordi Castro, 2007. "A Shortest-Paths Heuristic for Statistical Data Protection in Positive Tables," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 520-533, November.
    2. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    3. Castro, Jordi, 2006. "Minimum-distance controlled perturbation methods for large-scale tabular data protection," European Journal of Operational Research, Elsevier, vol. 171(1), pages 39-52, May.
    4. J. Benders, 2005. "Partitioning procedures for solving mixed-variables programming problems," Computational Management Science, Springer, vol. 2(1), pages 3-19, January.
    5. Matteo Fischetti & Juan José Salazar, 2001. "Solving the Cell Suppression Problem on Tabular Data with Linear Constraints," Management Science, INFORMS, vol. 47(7), pages 1008-1027, July.
    6. Michael Bacharach, 1966. "Matrix Rounding Problems," Management Science, INFORMS, vol. 12(9), pages 732-742, May.
    7. Salazar-Gonzalez, Juan-Jose, 2004. "Mathematical models for applying cell suppression methodology in statistical data protection," European Journal of Operational Research, Elsevier, vol. 154(3), pages 740-754, May.
    8. Jordi Castro, 2005. "Quadratic interior-point methods in statistical disclosure control," Computational Management Science, Springer, vol. 2(2), pages 107-121, March.
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    Cited by:

    1. Sage, Andrew J. & Wright, Stephen E., 2016. "Obtaining cell counts for contingency tables from rounded conditional frequencies," European Journal of Operational Research, Elsevier, vol. 250(1), pages 91-100.
    2. Hugo E. Caceres & Ben Moews, 2024. "Evaluating utility in synthetic banking microdata applications," Papers 2410.22519, arXiv.org.
    3. Kazuhiro Minami & Yutaka Abe, 2017. "Statistical Disclosure Control for Tabular Data in R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 67-76, December.
    4. Daniel Baena & Jordi Castro & Antonio Frangioni, 2020. "Stabilized Benders Methods for Large-Scale Combinatorial Optimization, with Application to Data Privacy," Management Science, INFORMS, vol. 66(7), pages 3051-3068, July.

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