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.
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