Multi-instance iris remote authentication using private multi-class perceptron on malicious cloud server

In recent years, biometric authentication system (BAS) has become the most promising and popular authentication system in identity management. Due to its capability to solve the limitations of unimodal systems, multi-biometric systems (MBS) have been extensively accepted in various fields. The main step in MBS is information fusion. On the other hand, directly storing the fused templates into a centralized server leads to privacy concerns. Recently, many BAS based on homomorphic encryption has been introduced to provide confidentiality for the fused templates. However, most of the existing solutions rely on an implication of the assumption that the server is “Honest-but-Curious”. As a result, the compromise of such server results into entire system vulnerability. To address this, we propose a novel P rivacy P reserving (PP) multi-instance iris remote authentication system to accord with attacks at the malicious server and over the transmission channel. Our scheme uses F ully H omomorphic E ncryption (FHE) to achieve the confidentiality of the fused iris templates and polynomial factorization algorithm to achieve the integrity of the matching result. We propose a PP iris authentication system using P rivate M ulti-C lass P erceptron (PMCP) by using the properties of FHE. Moreover, we propose C ontradistinguish S imilarity A nalysis (CSA), a feature level fusion technique that minimizes the between-class correlations and maximizes the pair-wise correlations. Our method has experimented on IITD and CASIA-V3-Interval iris databases to check the effectiveness and robustness. Experimental results show that our method provides improved accuracy, and eliminates the need to trust the cloud server when compared to the state-of-the-art approaches.