A Post-Quantum Cryptography Enabled Feature-Level Fusion Framework for Privacy-Preserving Multimodal Biometric Recognition

As quantum computing continues to advance, it threatens the long-term protection of traditional cryptographic methods, especially in biometric authentication systems where it is important to protect sensitive data. To overcome this challenge, we present a comprehensive, privacy-preserving framework for multimodal biometric authentication that can easily integrate any two binary-encoded modalities through feature-level fusion, ensuring that all sensitive information remains encrypted under a CKKS-based homomorphic encryption scheme resistant to both classical and quantum-enabled attacks. To demonstrate its versatility and effectiveness, we apply this framework to the retinal vascular patterns and palm vein features, which are inherently spoof-resistant and particularly well suited to high-security applications. This method not only ensures the secrecy of the combined biometric sample, but also enables the complete assessment of recognition performance and resilience against adversarial attacks. The results show that our approach provides protection against threats such as data leakage and replay attacks while maintaining high recognition performance and operational efficiency. These findings demonstrate the feasibility of integrating multimodal biometrics with post-quantum cryptography, giving a strong, privacy-oriented authentication solution suitable for mission-critical applications in the post-quantum era.