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Column generation bounds for numerical microaggregation

Author

Listed:
  • Daniel Aloise
  • Pierre Hansen
  • Caroline Rocha
  • Éverton Santi

Abstract

The biggest challenge when disclosing private data is to share information contained in databases while protecting people from being individually identified. Microaggregation is a family of methods for statistical disclosure control. The principle of microaggregation is that confidentiality rules permit the publication of individual records if they are partitioned into groups of size larger or equal to a fixed threshold value, where none is more representative than the others in the same group. The application of such rules leads to replacing individual values by those computed from small groups (microaggregates), before data publication. This work proposes a column generation algorithm for numerical microaggregation in which its pricing problem is solved by a specialized branch-and-bound. The algorithm is able to find, for the first time, lower bounds for instances of three real-world datasets commonly used in the literature. Furthermore, new best known solutions are obtained for these instances by means of a simple heuristic method with the columns generated. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Daniel Aloise & Pierre Hansen & Caroline Rocha & Éverton Santi, 2014. "Column generation bounds for numerical microaggregation," Journal of Global Optimization, Springer, vol. 60(2), pages 165-182, October.
    • Handle: RePEc:spr:jglopt:v:60:y:2014:i:2:p:165-182
    DOI: 10.1007/s10898-014-0149-3
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    References listed on IDEAS

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    1. Hansen, Pierre & Mladenovic, Nenad & Moreno Pérez, Jos´e A., 2008. "Variable neighborhood search," European Journal of Operational Research, Elsevier, vol. 191(3), pages 593-595, December.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    3. J. L. Goffin & A. Haurie & J. P. Vial, 1992. "Decomposition and Nondifferentiable Optimization with the Projective Algorithm," Management Science, INFORMS, vol. 38(2), pages 284-302, February.
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