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FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments

Fuyi Wang, Fangyuan Sun, Mingyuan Fan, Jianying Zhou, Jin Ma, Chao Chen, Jiangang Shu, Leo Yu Zhang

TL;DR

FLAME addresses the need for privacy-preserving biometric authentication in malicious environments by introducing a two-server, 2PC scheme that leverages lightweight secret-sharing primitives and an offline-online paradigm. It supports multiple similarity metrics (Cosine and Euclidean) through client-side preprocessing that aligns their semantics with a unified server-side inner-product-based authentication, while maintaining strong integrity guarantees via MAC-checked protocols. The paper formalizes secure constructions for inner products and comparisons, proves security under malicious adversaries, and demonstrates substantial practical gains: dramatic online-time reductions and orders-of-magnitude reductions in online communication compared to prior work, with accuracy above practical thresholds on real facial biometrics datasets. These results indicate FLAME's suitability for real-time, large-scale biometric authentication in cloud-assisted settings, offering strong privacy, robustness, and flexibility for diverse biometric traits and similarity measures.

Abstract

Privacy-preserving biometric authentication (PPBA) enables client authentication without revealing sensitive biometric data, addressing privacy and security concerns. Many studies have proposed efficient cryptographic solutions to this problem based on secure multi-party computation, typically assuming a semi-honest adversary model, where all parties follow the protocol but may try to learn additional information. However, this assumption often falls short in real-world scenarios, where adversaries may behave maliciously and actively deviate from the protocol. In this paper, we propose, implement, and evaluate $\sysname$, a \underline{F}lexible and \underline{L}ightweight biometric \underline{A}uthentication scheme designed for a \underline{M}alicious \underline{E}nvironment. By hybridizing lightweight secret-sharing-family primitives within two-party computation, $\sysname$ carefully designs a line of supporting protocols that incorporate integrity checks with rationally extra overhead. Additionally, $\sysname$ enables server-side authentication with various similarity metrics through a cross-metric-compatible design, enhancing flexibility and robustness without requiring any changes to the server-side process. A rigorous theoretical analysis validates the correctness, security, and efficiency of $\sysname$. Extensive experiments highlight $\sysname$'s superior efficiency, with a communication reduction by {$97.61\times \sim 110.13\times$} and a speedup of {$ 2.72\times \sim 2.82\times$ (resp. $ 6.58\times \sim 8.51\times$)} in a LAN (resp. WAN) environment, when compared to the state-of-the-art work.

FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments

TL;DR

FLAME addresses the need for privacy-preserving biometric authentication in malicious environments by introducing a two-server, 2PC scheme that leverages lightweight secret-sharing primitives and an offline-online paradigm. It supports multiple similarity metrics (Cosine and Euclidean) through client-side preprocessing that aligns their semantics with a unified server-side inner-product-based authentication, while maintaining strong integrity guarantees via MAC-checked protocols. The paper formalizes secure constructions for inner products and comparisons, proves security under malicious adversaries, and demonstrates substantial practical gains: dramatic online-time reductions and orders-of-magnitude reductions in online communication compared to prior work, with accuracy above practical thresholds on real facial biometrics datasets. These results indicate FLAME's suitability for real-time, large-scale biometric authentication in cloud-assisted settings, offering strong privacy, robustness, and flexibility for diverse biometric traits and similarity measures.

Abstract

Privacy-preserving biometric authentication (PPBA) enables client authentication without revealing sensitive biometric data, addressing privacy and security concerns. Many studies have proposed efficient cryptographic solutions to this problem based on secure multi-party computation, typically assuming a semi-honest adversary model, where all parties follow the protocol but may try to learn additional information. However, this assumption often falls short in real-world scenarios, where adversaries may behave maliciously and actively deviate from the protocol. In this paper, we propose, implement, and evaluate , a \underline{F}lexible and \underline{L}ightweight biometric \underline{A}uthentication scheme designed for a \underline{M}alicious \underline{E}nvironment. By hybridizing lightweight secret-sharing-family primitives within two-party computation, carefully designs a line of supporting protocols that incorporate integrity checks with rationally extra overhead. Additionally, enables server-side authentication with various similarity metrics through a cross-metric-compatible design, enhancing flexibility and robustness without requiring any changes to the server-side process. A rigorous theoretical analysis validates the correctness, security, and efficiency of . Extensive experiments highlight 's superior efficiency, with a communication reduction by {} and a speedup of { (resp. )} in a LAN (resp. WAN) environment, when compared to the state-of-the-art work.

Paper Structure

This paper contains 15 sections, 5 theorems, 9 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Secret Sharing
  3. System Overview
  4. Supporting Protocols
  5. Secure Inner Product Protocol
  6. Secure Comparison Protocol
  7. The Design of FLAME
  8. Client-side Pre-processing and Outsourcing
  9. Server-side Secure Authentication
  10. Theoretical Analysis
  11. Experimental Evaluation
  12. Micro-benchmarks on Supporting Protocols
  13. Evaluations on $\textsf{FLAME}$ Scheme
  14. Accuracy
  15. Conclusion and Future Work

Key Result

Theorem 1

For any OptSS-shared input vectors $(\Delta_X,\left<\lambda_X\right>)$, $(\Delta_{\phi X},\left<\lambda_{\phi X}\right>)$, and $(\Delta_Y,\left<\lambda_Y\right>)$, the $\prod_{\mathtt{SecIP}}$ yields the correct results $(\Delta_z,\left<\lambda_z\right>)$ and $(\Delta_{\phi z},\left<\lambda_{\phi z}

Figures (9)

  • Figure 1: The system model.
  • Figure 2: Functionality $\mathcal{F}_f$.
  • Figure 3: Construction of secure inner product protocol $\prod_{\mathtt{SecIP}}$.
  • Figure 4: The batch MAC check protocol.
  • Figure 5: Construction of secure comparison protocol $\prod_{\mathtt{SecCMP}}$.
  • ...and 4 more figures

Theorems & Definitions (11)

  • Definition 1
  • Theorem 1: The correctness of $\prod_{\mathtt{SecIP}}$
  • proof
  • Theorem 2: The correctness of $\prod_{\mathtt{SecCMP}}$
  • proof
  • Theorem 3
  • proof : Proof Sketch
  • Theorem 4
  • proof
  • Theorem 5
  • ...and 1 more