TL;DR
This paper introduces DUEF-GA, a comprehensive framework for evaluating data utility and privacy in graph anonymization, enabling consistent comparison of anonymization methods through diverse metrics.
Contribution
The authors propose a unified evaluation framework for graph anonymization that incorporates generic and task-specific utility metrics, along with re-identification risk assessment.
Findings
Framework provides objective scores for method comparison
Includes metrics for information loss and re-identification risk
Helps optimize anonymization parameters
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 used to evaluate data utility and information loss in the anonymized graphs. However, there is no consensus about how to evaluate data utility and information loss in privacy-preserving and anonymization scenarios, where the anonymous datasets were perturbed to hinder re-identification processes. Authors use diverse metrics to evaluate data utility and, consequently, it is 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. Our framework includes metrics based on generic information loss measures, such as average distance…
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