Adaptive selection method of differential privacy GAN gradient clipping thresholds

Abstract

A method of adaptive selection of differential privacy GAN gradient clipping threshold was proposed.The method assumes that a small portion of public data that is identically distributed with the private data can be accessed,a batch of data is randomly selected from the public data,a clipping threshold is set as an average gradient norm of the batch of data,and the above operations are iterated until the network converges.The method was verified on the Mnist and Cifar10 data sets.The results show that under a reasonable privacy budget,the accuracy of CNN classifiers is improved by 1%~4% compared with the differential privacy auxiliary classifier GAN,inception scores increased by 0.6~1.2.

Keywords

• gan
• differential privacy protection
• gradient clipping thresholds
• adaptive selection