Clustering-Based Categorical Data Protection

  • Jordi Marés
  • Vicenç Torra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7556)


The need of improving the privacy on public datasets is becoming more and more important because the number of public available datasets is growing very fast. This forced the continuous research to find better protection methods that prevent the disclosure of the entities or individuals in a dataset while preserving the data utility.

In this paper we present a new approach for categorical data protection based on applying clustering to the dataset and then protecting each cluster. We show that this new approach allow us to have protections with better trade-off between data utility and individuals information disclosure.


Clustering Categorical data Data privacy Microaggregation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, C.C., Yu, P.S.: Privacy-Preserving Data Mining: Models and Algorithms. Springer (2008)Google Scholar
  2. 2.
    Bonchi, F., Ferrari, E.: Privacy-aware knowledge discovery. CRC Press (2011)Google Scholar
  3. 3.
    Defays, D., Nanopoulos, P.: Panels of enterprises and confidentiality: The small aggregates method. In: Proceedings of the 1992 Symposium on Design and Analysis of Longitudinal Surveys, pp. 195–204. Statistics Canada, Ottawa (1993)Google Scholar
  4. 4.
    Domingo-Ferrer, J., Torra, V.: A quantitative comparison of disclosure control methods for microdata. In: Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 111–133. Elsevier (2001)Google Scholar
  5. 5.
    Domingo-Ferrer, J., Torra, V.: Distance-based and probabilistic record linkage for re-identification of records with categorical variables. In: Butlletí de l’ÀCIA, vol. 28, pp. 243–250. Associació Catalana d’Intelligència Artificial (2002)Google Scholar
  6. 6.
    Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. In: IEEE Transactions on Knowledge and Data Engineering, vol. 14, pp. 189–201. IEEE Press, New York (2002)Google Scholar
  7. 7.
    Domingo-Ferrer, J., Gonzlez-Nicols, U.: Hybrid microdata using microaggregation. Information Sciences 180(15), 2834–2844 (2010)Google Scholar
  8. 8.
    Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall (1988)Google Scholar
  9. 9.
    Kennard, R., Martin, L.: Computer Aided Design of Experiments. Technometrics 11(1), 137–148 (1969)Google Scholar
  10. 10.
    Kooiman, P., Willenborg, L., Gouweleeuw, J.: PRAM: A method for disclosure limitation of microdata. CBS research paper 9705 (1998)Google Scholar
  11. 11.
    LeFevre, K., DeWitt, D., Ramakrishnan, R.: Mondrian Multidimensional K-Anonymity. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006). IEEE Computer Society, Washington, DC (2006)Google Scholar
  12. 12.
    Nin, J., Herranz, J., Torra, V.: Rethinking rank swapping to decrease disclosure risk. Data Knowledge and Engineering 64, 346–364 (2008)Google Scholar
  13. 13.
    Oganian, A., Domingo-Ferrer, J.: On the complexity of microaggregation. In: Second Joint UNECE-Eurostat Work Session on Statistical Data Confidentiality, Skopje (2001)Google Scholar
  14. 14.
    Samarati, P.: Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)Google Scholar
  15. 15.
    Torra, V., Domingo-Ferrer, J.: Disclosure control methods and information loss for microdata, pp. 91–110. Elsevier (2001)Google Scholar
  16. 16.
    Torra, V.: Microaggregation for Categorical Variables: A Median Based Approach. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 162–174. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    UCI machine learning repository,
  18. 18.
    Willenborg, L., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics. Springer (2001)Google Scholar
  19. 19.
    Winkler, W.E.: Re-identification Methods for Masked Microdata. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 216–230. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Yancey, W.E., Winkler, W.E., Creecy, R.H.: Disclosure Risk Assessment in Perturbative Microdata Protection. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 135–152. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jordi Marés
    • 1
  • Vicenç Torra
    • 1
  1. 1.IIIA - Institut d’Investigació en Intel·ligència Artificial, CSIC - Consejo Superior de Investigaciones CientíficasUniversitat Autònoma de Barcelona (UAB)BellaterraSpain

Personalised recommendations