Pattern-Guided Data Anonymization and Clustering

  • Robert Bredereck
  • André Nichterlein
  • Rolf Niedermeier
  • Geevarghese Philip
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6907)

Abstract

A matrix M over a fixed alphabet is k-anonymous if every row in M has at least k − 1 identical copies in M. Making a matrix k-anonymous by replacing a minimum number of entries with an additional ⋆-symbol (called “suppressing entries”) is known to be NP-hard. This task arises in the context of privacy-preserving publishing. We propose and analyze the computational complexity of an enhanced anonymization model where the user of the k-anonymized data may additionally “guide” the selection of the candidate matrix entries to be suppressed. The basic idea is to express this by means of “pattern vectors” which are part of the input. This can also be interpreted as a sort of clustering process. It is motivated by the observation that the “value” of matrix entries may significantly differ, and losing one (by suppression) may be more harmful than losing the other, which again may very much depend on the intended use of the anonymized data. We show that already very basic special cases of our new model lead to NP-hard problems while others allow for (fixed-parameter) tractability results.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving anonymity via clustering. ACM Trans. Algorithms 6(3), 1–19 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Blocki, J., Williams, R.: Resolving the complexity of some data privacy problems. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010. LNCS, vol. 6199, pp. 393–404. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Bodlaender, H.L.: Kernelization: New upper and lower bound techniques. In: Chen, J., Fomin, F.V. (eds.) IWPEC 2009. LNCS, vol. 5917, pp. 17–37. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Bodlaender, H.L., Thomassé, S., Yeo, A.: Analysis of data reduction: Transformations give evidence for non-existence of polynomial kernels. Technical Report UU-CS-2008-030, Department of Information and Computing Sciences, Utrecht University (2008)Google Scholar
  5. 5.
    Bredereck, R., Nichterlein, A., Niedermeier, R., Philip, G.: The effect of homogeneity on the complexity of k-anonymity. In: Proc. 18th FCT. LNCS, Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Dom, M., Lokshtanov, D., Saurabh, S.: Incompressibility through colors and iDs. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009. LNCS, vol. 5555, pp. 378–389. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Proc. 3rd ARES, pp. 990–993. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  8. 8.
    Fellows, M.R.: Towards fully multivariate algorithmics: Some new results and directions in parameter ecology. In: Fiala, J., Kratochvíl, J., Miller, M. (eds.) IWOCA 2009. LNCS, vol. 5874, pp. 2–10. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:14 (2010)CrossRefGoogle Scholar
  10. 10.
    Guo, J., Niedermeier, R.: Invitation to data reduction and problem kernelization. ACM SIGACT News 38(1), 31–45 (2007)CrossRefGoogle Scholar
  11. 11.
    Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proc. 23rd PODS, pp. 223–228. ACM, New York (2004)Google Scholar
  12. 12.
    Niedermeier, R.: Reflections on multivariate algorithmics and problem parameterization. In: Proc. 27th STACS. LIPIcs, vol. 5, pp. 17–32. IBFI Dagstuhl (2010)Google Scholar
  13. 13.
    Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. IJUFKS 10(5), 571–588 (2002)MathSciNetMATHGoogle Scholar
  14. 14.
    Sweeney, L.: k-anonymity: A model for protecting privacy. IJUFKS 10(5), 557–570 (2002)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2011

Authors and Affiliations

  • Robert Bredereck
    • 1
  • André Nichterlein
    • 1
  • Rolf Niedermeier
    • 1
  • Geevarghese Philip
    • 2
  1. 1.Institut für Softwaretechnik und Theoretische InformatikTU BerlinGermany
  2. 2.The Institute of Mathematical SciencesChennaiIndia

Personalised recommendations