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.