Anonymizing graphs: measuring quality for clustering

Producció científica: Contribució a una revistaArticleRecercaAvaluat per experts

13 Cites (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.
Idioma originalEnglish
Pàgines (de-a)507-528
RevistaKnowledge and Information Systems
Estat de la publicacióPublicada - 17 de set. 2015


Navegar pels temes de recerca de 'Anonymizing graphs: measuring quality for clustering'. Junts formen un fingerprint únic.

Com citar-ho