  Generative Adversarial Network (GAN) and its variants have recently attracted
intensive research interests due to their elegant theoretical foundation and
excellent empirical performance as generative models. These tools provide a
promising direction in the studies where data availability is limited. One
common issue in GANs is that the density of the learned generative distribution
could concentrate on the training data points, meaning that they can easily
remember training samples due to the high model complexity of deep networks.
This becomes a major concern when GANs are applied to private or sensitive data
such as patient medical records, and the concentration of distribution may
divulge critical patient information. To address this issue, in this paper we
propose a differentially private GAN (DPGAN) model, in which we achieve
differential privacy in GANs by adding carefully designed noise to gradients
during the learning procedure. We provide rigorous proof for the privacy
guarantee, as well as comprehensive empirical evidence to support our analysis,
where we demonstrate that our method can generate high quality data points at a
reasonable privacy level.
