Privacy Preserving Synthetic Health Data We examine the feasibility of using synthetic medical data generated by GANs in the classroom, to teach data science in health informatics. We present an endtoend methodology to retain instructional utility, while preserving privacy to a level, which meets regulatory requirements: (1) a GAN is trained by a certified medicaldata securityaware agent, inside a secure environment; (2) the final GAN model is used outside of the secure environment by external users (instructors or researchers) to generate synthetic data. This second step facilitates data handling for external users, by avoiding deidentification, which may require special user training, be costly, andor cause loss of data fidelity. We benchmark our proposed GAN versus various baseline methods using a novel set of metrics. At equal levels of privacy and utility, GANs provide small footprint models, meeting the desired specifications of our application domain. Data, code, and a challenge that we organized for educational purposes are available.

PPGAN: PrivacyPreserving Generative Adversarial Network Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of highquality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semanticrich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacypreserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding welldesigned noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate highquality synthetic data while retaining the required data available under a reasonable privacy budget.