Pattern-Guided k-Anonymity

  • Robert Bredereck
  • André Nichterlein
  • Rolf Niedermeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7924)


We suggest a user-oriented approach to combinatorial data anonymization. A data matrix is called k-anonymous if every row appears at least k times—the goal of the NP-hard k -Anonymity problem then is to make a given matrix k-anonymous by suppressing (blanking out) as few entries as possible. We describe an enhanced k-anonymization problem called Pattern-Guided k -Anonymity where the users can express the differing importance of various data features. We show that Pattern-Guided k -Anonymity remains NP-hard. We provide a fixed-parameter tractability result based on a data-driven parameterization and, based on this, develop an exact ILP-based solution method as well as a simple but very effective greedy heuristic. Experiments on several real-world datasets show that our heuristic easily matches up to the established “Mondrian” algorithm for k -Anonymity in terms of quality of the anonymization and outperforms it in terms of running time.


Integer Linear Program Input Matrix Greedy Heuristic Integer Linear Program Formulation Pattern Vector 
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 Berlin Heidelberg 2013

Authors and Affiliations

  • Robert Bredereck
    • 1
  • André Nichterlein
    • 1
  • Rolf Niedermeier
    • 1
  1. 1.Institut für Softwaretechnik und Theoretische InformatikTU BerlinBerlinGermany

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