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Towards Fuzzy c-means Based Microaggregation

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

Abstract

National Statistical Offices collect data from respondents and then publishes them. To avoid disclosure, data is protected before the release. One of the existing masking methods is microaggregation. This method is based on obtaining a set of clusters (clustering stage) and then aggregating the values of the elements in the cluster (aggregation stage). In this work we propose the use of fuzzy c-means in the clustering stage.

Keywords

Membership Degree Follow Objective Function Aggregation Stage Disclosure Risk Fuzzy Solution 
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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References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Felso, F., Theeuwes, J., Wagner, G. G., (2001), Disclosure Limitation Methods in Use: Results of a Survey, 17–42, 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.
    Klir, G., Yuan, B., (1995), Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, U.K.MATHGoogle Scholar
  6. 6.
    Kolen, J. F., Hutcheson, T., (2002), Reducing the time complecity of the Fuzzy C-Means Algorithm, IEEE Trans. on Fuzzy Systems, April, 263–267.Google Scholar
  7. 7.
    Miyamoto, S., Umayahara, K., (2000), Methods in Hard and Fuzzy Clustering, pp 85–129 in Z.-Q. Liu, S. Miyamoto (Eds.), Soft Computing and Human-Centered Machines, Springer-Tokyo.Google Scholar
  8. 8.
    Willenborg, L., De Waal, T., (1996), Statistical Disclosure Control in Practice, Springer LNS 111.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Josep Domingo-Ferrer
    • 1
  • Vicenç Torra
    • 2
  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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