  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.
