Privacy-Preserving Biometric Authentication Framework for Distributed Biotech Facilities Based on Secure Multi-Party Computation

Abstract

With the rapid digitalization of biotechnology facilities, biometric authentication has emerged as a crucial mechanism for ensuring secure access to sensitive research environments and genomic data repositories. However, the deployment of biometric systems raises significant privacy concerns, especially in distributed infrastructures where data exchange occurs across multiple sites. This paper proposes a privacy-preserving biometric authentication framework grounded in secure multi-party computation (SMPC) to address these challenges. The framework enables decentralized verification of biometric identifiers such as fingerprints, facial recognition, and iris scans without exposing raw biometric data to any single party. By leveraging SMPC protocols, biometric features are encrypted, partitioned, and collaboratively computed across multiple nodes, ensuring both data confidentiality and authentication accuracy. A prototype system was implemented and tested in simulated distributed biotech facility environments. Experimental results demonstrate that the proposed framework achieves a balance between security, computational efficiency, and scalability, with less than 8% overhead compared to conventional centralized biometric systems. This study contributes to the advancement of secure digital infrastructure in the biotechnology sector, offering a blueprint for safeguarding sensitive biological data while maintaining robust identity verification processes.