Data Utility Evaluation Framework for Graph Anonymization

Abstract

Anonymization of graph-based data is a problem which has been widely studied over the 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. However, there is no consensus about how to evaluate perturbation and data utility in privacypreserving and anonymization scenarios, where released datasets contain some noise to hinder re-identification processes. Thus, it is quite complex to compare different methods or algorithms in literature. In this paper we propose a framework to evaluate and compare anonymous datasets in a common way, providing an objective score to clearly compare methods and algorithms.