A Tool for Analyzing and Fixing Infeasible RCTA Instances

  • Jordi Castro
  • José A. González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6344)


Minimum-distance controlled tabular adjustment methods (CTA), and its restricted variants (RCTA), is a recent perturbative approach for tabular data protection. Given a table to be protected, the purpose of RCTA is to find the closest table that guarantees protection levels for the sensitive cells. This is achieved by adding slight adjustments to the remaining cells, possibly excluding a subset of them (usually, the total cells) which preserve their original values. If either protection levels are large, or the bounds for cell deviations are tight, or too many cell values have to be preserved, the resulting mixed integer linear problem may be reported as infeasible. This work describes a tool developed for analyzing infeasible instances. The tool is based on a general elastic programming approach, which considers an artificial problem obtained by relaxing constraints and bounds through the addition of extra elastic variables. The tool allows selecting the subset of constraints and bounds to be relaxed, such that an elastic filter method can be applied for isolating a subset of infeasible table relations, protection levels, and cell bounds. Some computational experiments are reported using real-world instances.


statistical disclosure control controlled tabular adjustment mixed integer linear programming infeasibility in optimization elastic constraints elastic filter 


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  1. 1.
    Castro, J.: Minimum-distance controlled perturbation methods for large-scale tabular data protection. European Journal of Operational Research 171, 39–52 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Castro, J.: A shortest paths heuristic for statistical disclosure control in positive tables. INFORMS Journal on Computing 19, 520–533 (2007)CrossRefGoogle Scholar
  3. 3.
    Castro, J.: Extending controlled tabular adjustment for non-additive tabular data with negative protection levels, Research Report DR 2010-01, Dept. of Statistics and Operations Research, Universitat Politècnica de Catalunya (2010) (submitted)Google Scholar
  4. 4.
    Castro, J., Giessing, S.: Testing variants of minimum distance controlled tabular adjustment. In: Monographs of Official Statistics, Eurostat-Office for Official Publications of the European Communities, Luxembourg, pp. 333–343 (2006)Google Scholar
  5. 5.
    Castro, J., González, J.A., Baena, D.: User’s and programmer’s manual of the RCTA package, Technical Report DR 2009-01, Dept. of Statistics and Operations Research, Universitat Politècnica de Catalunya (2009)Google Scholar
  6. 6.
    Cox, L.H., Kelly, J.P., Patil, R.: Balancing quality and confidentiality for multivariate tabular data. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 87–98. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Chinneck, J.W.: Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  8. 8.
    Chinneck, J.W., Dravnieks, E.W.: Locating minimal infeasible constraint sets in linear programs. ORSA Journal on Computing 3, 157–168 (1991)zbMATHGoogle Scholar
  9. 9.
    Dandekar, R.A., Cox, L.H.: Synthetic tabular Data: an alternative to complementary cell suppression. Energy Information Administration, U.S. (2002) (manuscript)Google Scholar
  10. 10.
    Giessing, S., Hundepool, A., Castro, J.: Rounding methods for protecting EU-aggregates. In: Eurostat methodologies and working papers. Worksession on statistical data confidentiality, Eurostat-Office for Official Publications of the European Communities, Luxembourg, 255–264 (2009)Google Scholar
  11. 11.
    Kelly, J.P., Golden, B.L., Assad, A.A.: Cell suppression: disclosure protection for sensitive tabular data. Networks 22, 28–55 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jordi Castro
    • 1
  • José A. González
    • 1
  1. 1.Department of Statistics and Operations ResearchUniversitat Politècnica de CatalunyaBarcelona

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