  Deep generative models, such as Generative Adversarial Networks (GANs),
synthesize diverse high-fidelity data samples by estimating the underlying
distribution of high dimensional data. Despite their success, GANs may disclose
private information from the data they are trained on, making them susceptible
to adversarial attacks such as membership inference attacks, in which an
adversary aims to determine if a record was part of the training set. We
propose an information theoretically motivated regularization term that
prevents the generative model from overfitting to training data and encourages
generalizability. We show that this penalty minimizes the JensenShannon
divergence between components of the generator trained on data with different
membership, and that it can be implemented at low cost using an additional
classifier. Our experiments on image datasets demonstrate that with the
proposed regularization, which comes at only a small added computational cost,
GANs are able to preserve privacy and generate high-quality samples that
achieve better downstream classification performance compared to non-private
and differentially private generative models.
