Aggregation Methods to Evaluate Multiple Protected Versions of the Same Confidential Data Set

  • Aïda Valls
  • Vicenç Torra
  • Josep Domingo-Ferrer
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 16)


This work is about disclosure risk for national statistical offices and, more particularly, for the case of releasing multiple protected versions of the same micro-data files. This is, several copies of a single original data file are released to several data users. Each user receives a protected copy, and the masking method for each copy is selected according to the research interests of the user: the selected masking method is such that it minimizes the information loss for his/her particular research.

Nevertheless, multiple releases of the same data increase the disclosure risk. This is so, because coalitions of data users can reconstruct original data and, thus, find the original (non-masked) information. In this work we propose a tool for evaluating this reconstruction.


Information Fusion Aggregation Operator Irrelevant Alternative Soft Computing Technique Linguistic Label 
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.
    Census Bureau, (1993), American Housing Survey 1993, Data publicly available from the U. S. Bureau of the Census through the Data Extraction System, Google Scholar
  2. 2.
    de Soto, A.R., Trillas, E., (1999), On antonym and negate in fuzzy logic, Int. J. of Int. Systems, 14: 3, 295–303CrossRefMATHGoogle Scholar
  3. 3.
    Domingo-Ferrer, J., Torra, V., (2001), A Quantitative Comparison of Disclosure Control Methods for Microdata, 111–133, in Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, P. Doyle, J. I. Lane, J. J. M. Theeuwes, L. M. Zayatz (Eds.), Elsevier.Google Scholar
  4. 4.
    Domingo-Ferrer, J., Torra, V., (2001), Disclosure Control Methods and Information Loss for Microdata, 91–110, in Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, P. Doyle, J. I. Lane, J. J. M. Theeuwes, L. M. Zayatz (Eds.), Elsevier.Google Scholar
  5. 5.
    Domingo-Ferrer, J., Torra, V., (2002), Aggregation techniques for statistical confidentiality, in “Aggregation operators: New trends and applications”, (Ed.), R. Mesiar, T. Calvo, G. Mayor, Physica-Verlag, Springer.Google Scholar
  6. 6.
    Domingo-Ferrer, J., Torra, V., (2002), On the Connections between Statistical Disclosure Control for Microdata and Some Artificial Intelligence Tools, submitted.Google Scholar
  7. 7.
    Domingo-Ferrer, J., Torra, V., Valls, A., (2002), Semantic based aggregation for statistical disclosure control, submitted.Google Scholar
  8. 8.
    Dubois, D., Koning, J-L., (1991), Social choice axioms for fuzzy set aggregation, Fuzzy Sets and Systems, vol. 43, pp. 257–274.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    F. Sebe, J. Domingo-Ferrer, J. M. Mateo-Sanz, V. Torra, Post-Masking optimization of the tradeoff between information loss and disclosure risk in masked microdata sets, Lecture Notes in Computer Science 2316, 163–171.Google Scholar
  10. 10.
    Torra, V., (1996), Negation functions based semantics for ordered linguistic labels, Int. J. of Intelligent Systems, 11 975–988.Google Scholar
  11. 11.
    Torra, Towards the re-identification of individuals in data files with common variables, Proc. of the 14th European Conference on Artificial Intelligence (ECAI2000), Berlin, Germany, 2000.Google Scholar
  12. 12.
    Torra, V., (2000), Re-identifying Individuals using OWA Operators, Proc. of the 6th Int. Conference on Soft Computing, Iizuka, Fukuoka, Japan, 2000.Google Scholar
  13. 13.
    Willenborg, L., De Waal, T., (1996), Statistical Disclosure Control in Practice, Springer LNS 111.Google Scholar
  14. 14.
    Yager, R. R., (1988), On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Trans. on SMC, 18 183–190.MathSciNetMATHGoogle Scholar
  15. 15.
    Valls, A., Moreno, A., Sanchez, D., A multi-criteria decision aid agent applied to the selection of the best receiver in a transplant, Proc. of the 4th Int. Conference on Enterprise Information Systems, ICEIS, 431–438, Ciudad Real, Spain, 2002.Google Scholar
  16. 16.
    Valls, A., Torra, V., (2000), Explaining the consensus of opinions with the vocabulary of the experts, Proc. IPMU 2000, Madrid, Spain, 2000.Google Scholar
  17. 17.
    Winkler, W. E., (1995), Advanced methods for record linkage, American Statistical Association, Proceedings of the Section on Survey Research Methods, pp. 467–472.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Aïda Valls
    • 1
  • Vicenç Torra
    • 2
  • Josep Domingo-Ferrer
    • 1
  1. 1.Dept. Comput. Eng. and Maths — ETSEUniversitat Rovira i VirgiliTarragona, CataloniaSpain
  2. 2.Institut d’Investigació en Intel·ligència Artificial — CSICBellaterra, CataloniaSpain

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