Design of a Blockchain-Powered Biometric Template Security Framework Using Augmented Sharding

Abstract

Biometric templates must be secured with traceability, immutability, and high-trust capabilities. A variety of system models are proposed by researchers, most of which either utilize blockchains or machine learning for improved security and quality of service (QoS). The augmented sharding model is designed using light weight incremental learning framework, which assists in shard formation and management. Performance evaluation of the proposed model indicates that it is able to achieve high accuracy attack mitigation, along with low block mining delay and high throughput. This performance is compared with various state-of-the-art methods and an improvement of 10% in terms of delay and 14% in terms of throughput is achieved. Further, an attack detection accuracy of 99.3% is obtained for sybil, masquerading, and man in the middle (MITM) attacks. This text further recommends improvement areas which can be further researched for enhancing security and QoS performance of the proposed model.