Exploit the Leak: Understanding Risks in Biometric Matchers

Fuente: arXiv
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Autori principali: Durbet, Axel, Thiry-Atighehchi, Kevin, Chagnon, Dorine, Grollemund, Paul-Marie
Natura: Preprint
Pubblicazione: 2023
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author Durbet, Axel
Thiry-Atighehchi, Kevin
Chagnon, Dorine
Grollemund, Paul-Marie
author_facet Durbet, Axel
Thiry-Atighehchi, Kevin
Chagnon, Dorine
Grollemund, Paul-Marie
contents In a biometric authentication or identification system, the matcher compares a stored and a fresh template to determine whether there is a match. This assessment is based on both a similarity score and a predefined threshold. For better compliance with privacy legislation, the matcher can be built upon a privacy-preserving distance. Beyond the binary output (`yes' or `no'), most schemes may perform more precise computations, e.g., the value of the distance. Such precise information is prone to leakage even when not returned by the system. This can occur due to a malware infection or the use of a weakly privacy-preserving distance, exemplified by side channel attacks or partially obfuscated designs. This paper provides an analysis of information leakage during distance evaluation. We provide a catalog of information leakage scenarios with their impacts on data privacy. Each scenario gives rise to unique attacks with impacts quantified in terms of computational costs, thereby providing a better understanding of the security level.
format Preprint
id arxiv_https___arxiv_org_abs_2307_13717
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Exploit the Leak: Understanding Risks in Biometric Matchers
Durbet, Axel
Thiry-Atighehchi, Kevin
Chagnon, Dorine
Grollemund, Paul-Marie
Cryptography and Security
Computer Vision and Pattern Recognition
In a biometric authentication or identification system, the matcher compares a stored and a fresh template to determine whether there is a match. This assessment is based on both a similarity score and a predefined threshold. For better compliance with privacy legislation, the matcher can be built upon a privacy-preserving distance. Beyond the binary output (`yes' or `no'), most schemes may perform more precise computations, e.g., the value of the distance. Such precise information is prone to leakage even when not returned by the system. This can occur due to a malware infection or the use of a weakly privacy-preserving distance, exemplified by side channel attacks or partially obfuscated designs. This paper provides an analysis of information leakage during distance evaluation. We provide a catalog of information leakage scenarios with their impacts on data privacy. Each scenario gives rise to unique attacks with impacts quantified in terms of computational costs, thereby providing a better understanding of the security level.
title Exploit the Leak: Understanding Risks in Biometric Matchers
topic Cryptography and Security
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2307.13717