A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases

  • Ebaa Fayyoumi
  • B. John Oommen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4302)


We consider the problem of securing statistical databases and, more specifically, the micro-aggregation technique (MAT), which coalesces the individual records in the micro-data file into groups or classes, and on being queried, reports, for the all individual values, the aggregated means of the corresponding group. This problem is known to be NP-hard and has been tackled using many heuristic solutions. In this paper we present the first reported Learning Automaton (LA) based solution to the MAT. The LA modifies a fixed-structure solution to the Equi-Partitioning Problem (EPP) to solve the micro-aggregation problem. The scheme has been implemented, rigorously tested and evaluated for different real and simulated data sets. The results clearly demonstrate the applicability of LA to the micro-aggregation problem, and to yield a solution that obtains a lower information loss when compared to the best available heuristic methods for micro-aggregation.


Information Loss Optimal Partition Individual Record Learning Cycle Learn Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adam, N., Wortmann, J.: Security-Control Methods for Statistical Databases: A comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)CrossRefGoogle Scholar
  2. 2.
    Baeyens, Y., Defays, D.: Estimation of Variance Loss Following Microaggregation by the Individual Ranking Method. In: Proceedings of Statistical Data Protection 1998, pp. 101–108. Office for Official Publications of the European Communities, Luxembourg (1999)Google Scholar
  3. 3.
    Domingo-Ferrer, J., Mateo-Sanz, J.: Practical Data-Oriented Microaggregation for Statistical Disclosure Control. IEEE Trans. on Know. and Data Eng. 14(1), 189–201 (2002)CrossRefGoogle Scholar
  4. 4.
    Mateo-Sanz, J., Domingo-Ferrer, J.: A Method for Data-Oriented Multivariate Microaggregation. In: Proceedings of Statistical Data Protection 1998, pp. 89–99. Office for Official Publications of the European Communities, Luxembourg (1999)Google Scholar
  5. 5.
    Hansen, S., Mukherjee, S.: A Polynomial Algorithm for Univariate Optimal Microaggregation. IEEE Trans. on Know. and Data Eng. 15(4), 1043–1044 (2003)CrossRefGoogle Scholar
  6. 6.
    Laszlo, M., Mukherjee, S.: Minimum Spanning Tree Partitioning Algorithm for Microaggregation. IEEE Trans. on Know. and Data Eng. 17(7), 902–911 (2005)CrossRefGoogle Scholar
  7. 7.
    Domingo-Ferrer, J., Torra, V.: Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation. Data Mining and Knowledge Discovery 11(2), 195–212 (2005)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Fayyoumi, E., Oommen, B. (Using Learning Automaton to Micro-Aggregate the Continuous Micro-data File) Unabridged Version of This PaperGoogle Scholar
  9. 9.
    Defays, D., Anwar, N.: Micro-Aggregation: A Generic Method. In: Proceedings of the 2nd International Symposium on Statistical Confidentiality, pp. 69–78. Office for Official Publications of the European Communities, Luxembourg (1995)Google Scholar
  10. 10.
    Defays, D., Nanopoulos, P.: Panels of Enterprises and Confidentiality: the Small Aggregates Method. In: Proceedings of 92 Symposium on Design and Analysis of Longitudinal Surveys, pp. 195–204. Statistics Canada, Ottawa (1993)Google Scholar
  11. 11.
    Mateo-Sanz, J., Domingo-Ferrer, J.: A Comparative Study of Microaggregation Methods. Questiio 22(3), 511–526 (1998)MATHGoogle Scholar
  12. 12.
    Solanas, A., Martínez-Ballesté, A.: V-MDAV: A Multivariate Microaggregation With Variable Group Size. In: 17th COMPSTAT Symposium of the IASC (2006)Google Scholar
  13. 13.
    Domingo-Ferrer, J., Mateo-Sanz, J.: On Resampling for Statistical Confidentiality in Contingency Tables. Computers and Mathematics with Applications 38, 13–32 (1999)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Li, Y., Zhu, S., Wang, L., Jajodia, S.: A privacy-enhanced microaggregation method. In: Eiter, T., Schewe, K.-D. (eds.) FoIKS 2002. LNCS, vol. 2284, pp. 148–159. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Fayyoumi, E., Oommen, B.: On Optimizing the k-Ward Micro-Aggregation Technique for Secure Statistical Databases (In: 11th Australasian Conference on Information Security and Privacy Proceeding)Google Scholar
  16. 16.
    Hundepool, A., Wetering, A., Ramaswamy, R., Franconi, L., Capobianchi, A., Wolf, P., Domingo-Ferrer, J., Torra, V., Brand, R., Giessing, S.: M-ARGUS Version 4.0 Software and User’s Manual (2004)Google Scholar
  17. 17.
    Gale, W., Das, S., Yu, C.: Improvements to an Algorithm for Equipartitioning. IEEE Trans. Comput. 39(5), 706–710 (1990)CrossRefGoogle Scholar
  18. 18.
    Oommen, B., Ma, D.: Deterministic Learning Automata Solutions to the Equipartitioning Problem. IEEE Transction Computer 37(1), 2–13 (1988)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Solanas, A., Martínez-Ballesté, A., Domingo-Ferrer, J., Mateo-Sanz, J.: A 2d-Tree-Based Blocking Method for Microaggregating Very Large Data Sets. In: The First International Conference on Availability,Reliability and Security. The International Dependability Conference Bridging Theory and Practice (2006)Google Scholar
  20. 20.
    Domingo-Ferrer, J., Torra, V.: A Quantitative Comparison of Disclosure Control Methods for Microdata. In: Doyle, P., Lane, J., Theeuwes, J., Zayatz, L. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, Amesterdam, North-Holland, pp. 113–134. Springer, Berlin (2002)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ebaa Fayyoumi
    • 1
  • B. John Oommen
    • 1
  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada

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