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Title: Anonymizing Graphs: Measuring Quality for Clustering
Author: Casas Roma, Jordi
Herrera Joancomartí, Jordi
Torra Reventós, Vicenç
Keywords: Privacy
Data Mining
Issue Date: 6-Aug-2014
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.
Description: Peer-reviewed
Language: English
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