  In this paper, we aim to understand the generalization properties of
generative adversarial networks (GANs) from a new perspective of privacy
protection. Theoretically, we prove that a differentially private learning
algorithm used for training the GAN does not overfit to a certain degree, i.e.,
the generalization gap can be bounded. Moreover, some recent works, such as the
Bayesian GAN, can be re-interpreted based on our theoretical insight from
privacy protection. Quantitatively, to evaluate the information leakage of
well-trained GAN models, we perform various membership attacks on these models.
The results show that previous Lipschitz regularization techniques are
effective in not only reducing the generalization gap but also alleviating the
information leakage of the training dataset.
