A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks

With the increase in applications of face verification, increasing attention has been paid to their accuracy and security. To ensure both the accuracy and safety of these systems, this paper proposes an encrypted face-verification system. In this paper, face features are extracted using deep neural networks and then encrypted with the Paillier algorithm and saved in a data set. The framework of the whole system involves three parties: the client, data server, and verification server. The data server saves the encrypted user features and user ID, the verification server performs verification, and the client is responsible for collecting a requester's information and sending it to the servers. The information is transmitted among parties as cipher text, which means that no parties know the private keys except for the verification server. The proposed scheme is tested with two deep convolutional neural networks architectures on the labeled faces in the Wild and Faces94 data sets. The extensive experimental results, including results for identification and verification tasks, show that our approach can enhance the security of a recognition system with little decrease in accuracy. Therefore, the proposed system is efficient with respect to both the security and high verification accuracy.