Research on the Privacy Security of Face Recognition Technology

To solve the problem that privacy data are easy to leak in the application of face recognition technology in apps, a method which is based on differential privacy for privacy security protection is proposed. Firstly, Bayesian GAN is conducted to obtain the training data with the same distribution as the privacy data, and the algorithm of differential privacy is conducted to train the training data to obtain these labels with privacy protection. Then, based on the proposed lightface lightweight face recognition model, the tag with noise is generated, and the gradient descent is conducted on the recovered face feature vector from the attack. Finally, through the analysis of privacy loss, an accurate privacy protection boundary is provided. From the results of experiments, it could be known that the proposed privacy security protection method can effectively protect the parameter information of the face recognition model under the face recognition technology and reduce the recognition accuracy of the image recovered by the attacker. Compared with the privacy protection methods such as DPSGD and PATE, it has strong privacy protection ability and can be applied to the privacy protection of practical APP.