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 the 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.

中文翻译：

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DUEF-GA：用于图匿名化的数据实用程序和隐私评估框架

基于图的数据的匿名化是近年来已经广泛研究的问题，并且已经开发了几种匿名化方法。信息丢失度量已用于评估匿名图中的数据实用性和信息丢失。但是，关于如何在隐私保护和匿名化方案中评估数据实用性和信息丢失的问题尚未达成共识，在这些方案中，匿名数据集受到干扰，阻碍了重新识别过程。作者使用多种指标来评估数据效用，因此，比较文献中的不同方法或算法很复杂。在本文中，我们提出了一种以通用方式评估和比较匿名数据集的框架，提供了一个客观评分，可以清楚地比较方法和算法。我们的框架包括基于通用信息丢失度量（例如平均距离或中间性中心）以及特定于任务的信息丢失度量（例如社区检测或信息流）的指标。此外，我们提供了一些指标来检查重新识别和风险评估。我们证明了我们的框架可以帮助研究人员和从业人员选择最佳的参数化和/或算法，以减少信息丢失并最大化数据实用性。