A Secure And Scalable Architecture For Fingerprint Databases In Iot Ecosystems: Integration Of Zero Trust, Blockchain Logging, And Encrypted Microservices

Abstract

As IoT-based authentication systems rapidly advance, fingerprint biometrics have become crucial for identity verification. However, the incorporation of fingerprint databases within IoT environments poses considerable challenges regarding security, storage, and real-time data communication, particularly in resource-limited settings. Contemporary applications, ranging from smart homes to national identification systems, require highly secure and low-latency handling of fingerprint information. To address current security needs, fingerprint database architectures have progressed into multi-layered systems that incorporate various sophisticated mechanisms. Zero Trust Architecture (ZTA) mandates stringent, ongoing authentication alongside detailed role-based access control. End-to-End Encryption (E2EE) secures data during transmission and when stored using standards such as AES-256, RSA-4096, and TLS 1.3 with mutual authentication. Tokenization and cancelable biometrics alter fingerprint data to hinder reverse engineering and minimize the risk of data breaches. Systems are increasingly implemented as microservices within containerized platforms like Docker or Kubernetes, facilitating improved scalability, isolation, and fault tolerance. Immutable blockchain logging, commonly utilizing platforms like Hyperledger Fabric, guarantees unalterable records of database access and changes to fingerprint templates. Furthermore, AI-driven intrusion detection systems (IDS) monitor access patterns in real-time to identify spoofing attempts and insider threats. Together, these advancements provide a secure, scalable, and compliant foundation for fingerprint databases in contemporary IoT ecosystems.