  The wide-spread availability of rich data has fueled the growth of machine
learning applications in numerous domains. However, growth in domains with
highly-sensitive data (e.g., medical) is largely hindered as the private nature
of data prohibits it from being shared. To this end, we propose
Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which
allows releasing a sanitized form of the sensitive data with rigorous privacy
guarantees. In contrast to prior work, our approach is able to distort gradient
information more precisely, and thereby enabling training deeper models which
generate more informative samples. Moreover, our formulation naturally allows
for training GANs in both centralized and federated (i.e., decentralized) data
scenarios. Through extensive experiments, we find our approach consistently
outperforms state-of-the-art approaches across multiple metrics (e.g., sample
quality) and datasets.
