Knowledge and Information Systems

, Volume 44, Issue 3, pp 507–528

Anonymizing graphs: measuring quality for clustering

  • Jordi Casas-Roma
  • Jordi Herrera-Joancomartí
  • Vicenç Torra
Regular Paper

DOI: 10.1007/s10115-014-0774-7

Cite this article as:
Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. Knowl Inf Syst (2015) 44: 507. doi:10.1007/s10115-014-0774-7


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.


Privacy Networks Data mining Mining methods and algorithms  Quality and Metrics Semi-structured data and XML 

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Jordi Casas-Roma
    • 1
  • Jordi Herrera-Joancomartí
    • 2
  • Vicenç Torra
    • 3
  1. 1.Universitat Oberta de Catalunya (UOC)BarcelonaSpain
  2. 2.Universitat Autònoma de Barcelona (UAB)BellaterraSpain
  3. 3.Artificial Intelligence Research Institute (IIIA)Spanish National Research Council (CSIC)BellaterraSpain