Anonymizing graphs: measuring quality for clustering

Jordi Casas-Roma, Jordi Herrera-Joancomartí, Vicenç Torra

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


© 2014, Springer-Verlag London. 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.
Original languageEnglish
Pages (from-to)507-528
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 17 Sep 2015


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


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