A Secure Biometric Authentication Architecture for Blockchain-driven Cyber-physical Systems

Abstract

With the increased usage of cyber-physical systems (CPSs) in different critical domains, there is an emerging need for sound mechanisms for security and privacy. A deep learning-based biometric authentication system using feature extraction integrated with zero-knowledge proof and blockchain-based storage techniques is proposed for a secure authentication system providing better assurance in data privacy. The proposed architecture consists of three modular layers: a biometric processing layer responsible for extracting discriminative features using the FaceNet model, a cryptographic layer transforming these features into keys and generating ZKP-based proofs, and a blockchain layer for immutable authentication results storage. The system was tested on the Database of Faces, which resulted in 100% classification accuracy, reliably 194 integrated cryptographic functions through proof generation and verification times averaging 1.884 ms and 4.062 ms, respectively. Its compact proof size of 340 bytes speaks to the efficiency of the system. While these results clearly confirm the potential of such a system to be actually deployed in CPS applications, future work will address those challenges related to real-world conditions, such as diverse environments and additional biometric modalities. The proposed system enables a scalable and secure framework for CPS applications where privacy, transparency, and reliability are paramount.