  Generative Adversarial Networks (GANs) are one of the well-known 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 (DP-CGAN)
training framework based on a new clipping and perturbation strategy, which
improves the performance of the model while preserving privacy of the training
dataset. DP-CGAN 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 DP-CGAN can
generate visually and empirically promising results on the MNIST dataset with a
single-digit epsilon parameter in differential privacy.
