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Biometric data contains distinctive human traits such as facial features orgait patterns. The use of biometric data permits an individuation so exact thatthe data is utilized effectively in identification and authentication systems.But for this same reason, privacy protections become indispensably necessary.  Privacy protection is extensively afforded by the technique of anonymization.Anonymization techniques obfuscate or remove the sensitive personal data toachieve high levels of anonymity. However, the effectiveness of anonymizationrelies, in equal parts, on the effectiveness of the methods employed toevaluate anonymization performance.  In this paper, we assess the state-of-the-art methods used to evaluate theperformance of anonymization techniques for facial images and gait patterns. Wedemonstrate that the state-of-the-art evaluation methods have serious andfrequent shortcomings. In particular, we find that the underlying assumptionsof the state-of-the-art are quite unwarranted. When a method evaluating theperformance of anonymization assumes a weak adversary or a weak recognitionscenario, then the resulting evaluation will very likely be a grossoverestimation of the anonymization performance. Therefore, we propose astronger adversary model which is alert to the recognition scenario as well asto the anonymization scenario. Our adversary model implements an appropriatemeasure of anonymization performance. We improve the selection process for theevaluation dataset, and we reduce the numbers of identities contained in thedataset while ensuring that these identities remain easily distinguishable fromone another. Our novel evaluation methodology surpasses the state-of-the-artbecause we measure worst-case performance and so deliver a highly reliableevaluation of biometric anonymization techniques.

A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data