Biometric data is pervasively captured and analyzed. Using modern machine learning approaches, identity and attribute inferences attacks have proven high accuracy. Anonymizations aim to mitigate such disclosures by modifying data in a way that prevents identification. However, the effectiveness of some anonymizations is unclear. Therefore, improvements of the corresponding evaluation methodology have been proposed recently. In this paper, we introduce SEBA, a framework for strong evaluation of biometric anonymizations. It combines and implements the state-of-the-art methodology in an easy-to-use and easy-to-expand software framework. This allows anonymization designers to easily test their techniques using a strong evaluation methodology. As part of this discourse, we introduce and discuss new metrics that allow for a more straightforward evaluation of the privacy-utility trade-off that is inherent to anonymization attempts. Finally, we report on a prototypical experiment to demonstrate SEBA's applicability.

翻译：生物特征数据被广泛采集与分析。利用现代机器学习方法，身份与属性推断攻击已被证明具有高准确性。匿名化技术旨在通过修改数据以防止身份识别，从而缓解此类信息泄露。然而，部分匿名化方法的有效性尚不明确。因此，近期已有针对相应评估方法的改进方案被提出。本文介绍了SEBA，一个用于生物特征匿名化强评估的框架。该框架整合并实现了当前最先进的评估方法，构建了一个易于使用且易于扩展的软件框架。这使得匿名化设计者能够借助强评估方法轻松测试其技术。在此论述中，我们引入并讨论了新的评估指标，这些指标能够更直接地评估匿名化尝试中固有的隐私-效用权衡关系。最后，我们通过原型实验报告展示了SEBA的适用性。