A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol

Abstract

Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.

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“…Eine Möglichkeit, um dieses Problem zu lösen, wären biometrische Zero-Knowledge-Proof (ZKP) Verfahren (vgl. Tran et al 2022;Sakashita et al 2009), die es dem Sensor ermöglichen würden, zu beweisen, dass er gerade die Biometrie einer bestimmten Person gemessen hat, ohne die Messdaten an den Identity Agent weiterzugeben. Allerdings sind diese ZKP-Verfahren nur für den 1 W 1-Vergleich, also die biometrische Authentifizierung einer Person und nicht für den 1 W N -Abgleich, also die biometrische Identifizierung geeignet.…”

An Intelligent System for Fingerprint Recognition and Verification Using Dilated Convolutional Neural Network and Extreme Learning Machine

“…The evaluations showed that the system attained an average equal error rate of 2.01% on FVC2004 fingerprint datasets, which was better than previous state-of-the-art methods. Quang Nhat Tran et al [15] suggested a binary classification system for biometric authentication with a zero-knowledge proof protocol. The features of the fingerprint data were represented in a binary form initially.…”

Continuous Nonintrusive Mobile Device Soft Keyboard Biometric Authentication

“…Ryu et al [33] gave a survey of continuous multimodal biometric authentication systems. Another recent development [34] proposed a zero-knowledge proof protocol for biometric authentication.…”