Statistical Analysis and Optimization of the MFA Protecting Private Keys

TL;DR

This paper introduces a statistical approach to optimize multi-factor authentication (MFA) schemes that protect private keys, using biometric features, SRAM PUF tokens, and passwords to enhance security and accuracy.

Contribution

It proposes a novel bit-truncation method for biometric responses and a statistical analysis to optimize MFA factors, improving key security and reducing error rates.

Findings

Reduced false-reject and false-acceptance rates

Generated error-free ephemeral keys

Enhanced security of private key protection

Abstract

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.