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)


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.


Pattern Cluster Input Matrix Hair Color Matrix Entry Pattern Mask 
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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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

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