Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication

Abstrak

This paper introduces a novel framework designed to bolster privacy protections within automated voice authentication systems, addressing mounting concerns as voice-based authentication grows in prominence. The widespread adoption of these systems has underscored apprehensions regarding the storage and processing of sensitive voice biometric data without adequate safeguards. To mitigate these risks, a modified framework is proposed, aiming to enhance privacy without compromising authentication accuracy and efficiency. Three key techniques are implemented to address these challenges. Firstly, advanced encryption methods are employed for secure voice data storage and transmission, through the homomorphic encryption to enable authentication processing on encrypted data. Secondly, a privacy-preserving feature extraction method is introduced, transforming raw voice inputs into irreversible representations to shield original biometric information. Additionally, the framework incorporates differential privacy mechanisms, adding controlled noise to aggregated voice data to prevent individual identification within large datasets. A user-centric consent and control model is proposed, empowering individuals to manage their voice profiles and authentication settings. Experimental findings demonstrate that the framework achieves enhanced authentication accuracy while markedly reducing privacy risks compared to conventional systems. This contribution addresses the ongoing challenge of balancing security and privacy in biometric authentication technologies.