Statistical Analysis and Optimization of the MFA Protecting Private Keys
This work provides an incremental improvement in the security of private keys for users of asymmetrical cryptography, particularly in financial transactions and cryptocurrencies.
This paper addresses the problem of protecting private keys in asymmetrical cryptography by proposing a novel bit-truncation method for generating ephemeral keys from facial features. This method significantly improves accuracy and security, resulting in reduced false-reject and false-acceptance rates and error-free ephemeral key generation within a multi-factor authentication (MFA) scheme.
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.