Abstract
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.
Highlights
Since the new century, the application of the Internet industry and the rapid development of computer technology have brought great convenience to data sharing and increased the risk of privacy leakage
Erefore, this study uses Bayesian generative adversarial networks (GANs) in deep learning to obtain the training data with the same distribution as the privacy data and proposes a privacy security protection method of differential privacy. e lightface lightweight face recognition model is constructed to generate labels with noise, the gradient descent is conducted on the recovered face feature vector from the attack, and the privacy loss analysis is used to obtain the accurate privacy protection boundary so as to realize the privacy protection under the face recognition technology
This study proposes a privacy protection method based on differential privacy. rough the Bayesian GAN and differential privacy algorithm, tags with privacy protection can be obtained to prevent attackers from accessing the privacy data training model directly
Summary
The application of the Internet industry and the rapid development of computer technology have brought great convenience to data sharing and increased the risk of privacy leakage. Experiments are carried out in five commonly used face recognition databases It could be known from the results that the abovementioned method is superior to the traditional regression model [3]. Liu et al came up with a method of face image recognition which is based on singular value processing, so as to broaden the data sample set [9]. It can be seen from the abovementioned research that the current technology for face recognition mainly starts from three aspects: one is the feature extraction of the face image; the other is the image features classification; and the third is the image sample processing.
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