Tickling Deep Differential Privacy Protection Method Based on DCGAN

To prevent attackers from using generative adversarial networks (GAN) and other technologies in the application of deep learning model to restore the data in the training dataset, and to protect the sensitive information of users in the training dataset, a ticking deep differential privacy protection method was proposed based on DCGAN. The noise data was added the differential privacy theory when optimizing the in-depth network parameters in this method. Then the privacy budget of each layer of the deep network was calculated in a stochastic gradient descent (SGD), which based on the combination of differential privacy and Gaussian distribution, Gaussian noise was added to minimize the total privacy budget in the stochastic gradient descent calculation. And then the optimal result that the attacker may obtain was generated by using DCGAN. Finally, in order to achieve balance between data availability and privacy protection, the difference among the attack result and the original data was used to adjust the deep differential privacy model. The results show that this method has high privacy protection ability for sensitive information in training dataset.