SEBA: Strong Evaluation of Biometric Anonymizations

TL;DR

SEBA is a comprehensive framework designed to rigorously evaluate biometric anonymization techniques, incorporating new metrics and facilitating easier testing of privacy-utility trade-offs.

Contribution

It introduces SEBA, an easy-to-use software framework that implements advanced evaluation methodologies and new metrics for assessing biometric anonymizations.

Findings

SEBA enables strong, standardized evaluation of anonymization methods.

New metrics improve assessment of privacy-utility trade-offs.

Demonstrated applicability through a prototypical experiment.

Abstract

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.