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Anonymizing graphs: measuring quality for clustering

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Abstract

Anonymization of graph-based data is a problem, which has been widely studied last years, and several anonymization methods have been developed. Information loss measures have been carried out to evaluate the noise introduced in the anonymized data. Generic information loss measures ignore the intended anonymized data use. When data has to be released to third-parties, and there is no control on what kind of analyses users could do, these measures are the standard ones. In this paper we study different generic information loss measures for graphs comparing such measures to the cluster-specific ones. We want to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes.

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Acknowledgments

This work was partly funded by the Spanish Government through projects TIN2011-27076-C03-02 “CO-PRIVACY”, CONSOLIDER INGENIO 2010 CSD2007-0004 “ARES” and TIN2010-15764 “N-KHRONOUS”.

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Authors and Affiliations

  1. Universitat Oberta de Catalunya (UOC), Barcelona, Spain

    Jordi Casas-Roma

  2. Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain

    Jordi Herrera-Joancomartí

  3. Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Bellaterra, Spain

    Vicenç Torra

Authors
  1. Jordi Casas-Roma
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  2. Jordi Herrera-Joancomartí
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  3. Vicenç Torra
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Correspondence to Jordi Casas-Roma.

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Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. Anonymizing graphs: measuring quality for clustering. Knowl Inf Syst 44, 507–528 (2015). https://doi.org/10.1007/s10115-014-0774-7

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  • DOI: https://doi.org/10.1007/s10115-014-0774-7

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