In the current information age, asymmetrical cryptography is widely used to protect information and financial transactions such as cryptocurrencies. The loss of private keys can have catastrophic consequences; therefore, effective MFA schemes are needed. In this paper, we focus on generating ephemeral keys to protect private keys. We propose a novel bit-truncation method in which the most significant bits (MSBs) of response values derived from facial features in a template-less biometric scheme are removed, significantly improving both accuracy and security. A statistical analysis is presented to optimize an MFA comprising at least three factors: template-less biometrics, an SRAM PUF-based token, and passwords. The results show a reduction in both false-reject and false-acceptance rates, and the generation of error-free ephemeral keys.

翻译：在当今信息时代，非对称密码学被广泛用于保护信息和加密货币等金融交易。私钥丢失可能带来灾难性后果，因此需要有效的多因素认证方案。本文聚焦于生成临时密钥以保护私钥，提出一种创新的比特截断方法：在无模板生物特征方案中，移除由面部特征生成的响应值的最高有效位，从而显著提升准确性与安全性。通过统计分析优化了包含至少三个因素的多因素认证方案：无模板生物特征识别、基于SRAM PUF的硬件令牌及密码。实验结果表明，该方法在降低误拒率与误接受率的同时，能够生成零错误的临时密钥。