DPCGAN: Differentially Private Synthetic Data and Label Generation. Generative Adversarial Networks (GANs) are one of the wellknown models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible. One of the main challenges in this area is to preserve the privacy of individuals who participate in the training of the GAN models. To address this challenge, we introduce a Differentially Private Conditional GAN (DPCGAN) training framework based on a new clipping and perturbation strategy, which improves the performance of the model while preserving privacy of the training dataset. DPCGAN generates both synthetic data and corresponding labels and leverages the recently introduced Renyi differential privacy accountant to track the spent privacy budget. The experimental results show that DPCGAN can generate visually and empirically promising results on the MNIST dataset with a singledigit epsilon parameter in differential privacy.

PPGAN: PrivacyPreserving Generative Adversarial Network Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of highquality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semanticrich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacypreserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding welldesigned noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate highquality synthetic data while retaining the required data available under a reasonable privacy budget.