DUEF-GA: Data Utility and Privacy Evaluation Framework for Graph Anonymization

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 or betweenness centrality, and also task-specific information loss measures, such as community detection or information flow. Additionally, we provide some metrics to examine re-identification and risk assessment. We demonstrate that our framework could help researchers and practitioners to select the best parametrization and/or algorithm to reduce information loss and maximize data utility.