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Cancelable biometric schemes for Euclidean metric and Cosine metric

Yubing Jiang, Peisong Shen, Li Zeng, Xiaojie Zhu, Di Jiang, Chi Chen · Cybersecurity · 2023

AbstractThe handy biometric data is a double-edged sword, paving the way of the prosperity of biometric authentication systems but bringing the personal privacy concern. To alleviate the concern, various biometric template protection schemes are proposed to protect the biometric template from information leakage. The preponderance of existing proposals is based on Hamming metric, which ignores the fact that predominantly deployed biometric recognition systems (e.g. face, voice, gait) generate real-valued templates, more applicable to Euclidean metric and Cosine metric. Moreover, since the emergence of similarity-based attacks, those schemes are not secure under a stolen-token setting. In this paper, we propose a succinct biometric template protection scheme to address such a challenge. The proposed scheme is designed for Euclidean metric and Cosine metric instead of Hamming distance. Mainly, the succinct biometric template protection scheme consists of distance-preserving, one-way, and obfuscation modules. To be specific, we adopt location sensitive hash function to realize the distance-preserving and one-way properties simultaneously and use the modulo operation to implement many-

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