Biometrics-Based Authenticated Key Exchange with Multi-Factor Fuzzy Extractor
Hong Yen Tran, Jiankun Hu, Wen Hu

TL;DR
This paper introduces a multi-factor fuzzy extractor combining biometrics and secrets to enhance security in authenticated key exchange, enabling mutual authentication and re-issuance of credentials even if compromised.
Contribution
It proposes a novel multi-factor fuzzy extractor and a secure protocol with features like mutual authentication, impersonation prevention, and credential re-issuance, surpassing existing methods.
Findings
Achieved a 0.04% EER on finger vein dataset
Authentication time of 0.93 seconds on average
Communication overhead of 448 bytes
Abstract
Existing fuzzy extractors and similar methods provide an effective way for extracting a secret key from a user's biometric data, but are susceptible to impersonation attack: once a valid biometric sample is captured, the scheme is no longer secure. We propose a novel multi-factor fuzzy extractor that integrates both a user's secret (e.g., a password) and a user's biometrics in the generation and reconstruction process of a cryptographic key. We then employ this multi-factor fuzzy extractor to construct personal identity credentials which can be used in a new multi-factor authenticated key exchange protocol that possesses multiple important features. First, the protocol provides mutual authentication. Second, the user and service provider can authenticate each other without the involvement of the identity authority. Third, the protocol can prevent user impersonation from a compromised…
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Taxonomy
TopicsBiometric Identification and Security
