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Statistical Analysis and Optimization of the MFA Protecting Private Keys

Mahafujul Alam, Julie B. Heynssens, Bertrand Francis Cambou

TL;DR

A bit-truncation method removes the most significant bits from facial-distance responses in a template-less biometric system, enhancing accuracy and security in a zero-knowledge multi-factor authentication scheme that generates ephemeral keys to protect private keys.

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.

Statistical Analysis and Optimization of the MFA Protecting Private Keys

TL;DR

A bit-truncation method removes the most significant bits from facial-distance responses in a template-less biometric system, enhancing accuracy and security in a zero-knowledge multi-factor authentication scheme that generates ephemeral keys to protect private keys.

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.
Paper Structure (20 sections, 1 theorem, 1 equation, 4 figures, 1 table, 2 algorithms)

This paper contains 20 sections, 1 theorem, 1 equation, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Enrollment Phase
  5. SRAM PUF Token
  6. Template-less Biometrics
  7. Ephemeral Key Generation
  8. Experimental Evaluation
  9. Template-less Biometrics Analysis
  10. SRAM PUF Token Analysis
  11. Key Bias Test
  12. Overall Result
  13. Security Analysis
  14. Resistance to Sequential Attacks
  15. Resistance to Insider Attacks
  16. ...and 5 more sections

Key Result

Theorem 1

Let $f$ be the cellwise merge operator defined by with $A,B\in\{0,1,\mathrm{X}\}^{n\times n}$ and $C\in\{0,1\}^{n\times n}$. Then $f$ is not injective.

Figures (4)

  • Figure 1: Protocol diagram for ephemeral key generation.
  • Figure 2: Comparison of histograms for different configurations for template-less biometrics based on (Distance bits, Chopped MSB).
  • Figure 3: Number of key errors reduces as we increase the number of enrollments for SRAM PUF Token.
  • Figure 4: No bias in the generated key.

Theorems & Definitions (3)

  • Theorem 1
  • proof : Proof 1 (Counting Argument)
  • proof : Proof 2 (Explicit Collision)