GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

Understand

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.