  Deep learning models have demonstrated superior performance in several
application problems, such as image classification and speech processing.
However, creating a deep learning model using health record data requires
addressing certain privacy challenges that bring unique concerns to researchers
working in this domain. One effective way to handle such private data issues is
to generate realistic synthetic data that can provide practically acceptable
data quality and correspondingly the model performance. To tackle this
challenge, we develop a differentially private framework for synthetic data
generation using R\'enyi differential privacy. Our approach builds on
convolutional autoencoders and convolutional generative adversarial networks to
preserve some of the critical characteristics of the generated synthetic data.
In addition, our model can also capture the temporal information and feature
correlations that might be present in the original data. We demonstrate that
our model outperforms existing state-of-the-art models under the same privacy
budget using several publicly available benchmark medical datasets in both
supervised and unsupervised settings.
