Securing Face ID: Privacy Preservation for Non-Retentive Face Recognition System

Facial recognition technology is increasingly integrated into various applications. While face recognition systems have streamlined the authentication process and reduced the need for manual verification, the advancement in artificial intelligence (AI) generating realistic-looking media raises significant privacy concerns due to the potential misuse of biometric data. While current security protocols ensure data protection, storing biometric data in the system is a latent risk. In cases where the stored data is compromised, the users are susceptible to attacks such as face swapping, deepfake and identity theft. To address this, this paper presents a privacy preserving algorithm that omits the need to store human raw biometric data in the system. This is achieved by utilizing a
new combination of Locality-Sensitive Hashing (LSH), salting, and RSA encryption for face recognition. The proposed method ensures data security by securely hashing and encrypting facial features while maintaining high recognition accuracy. The proposed framework is evaluated on the Labeled Faces in the
Wild (LFW) and achieves a comparable performance with the state-of-the-art techniques.