Title: Differentially Private Mixed-Type Data Generation For Unsupervised Learning. Abstract: In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of GANs.  This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that should satisfy the same statistical properties as the original data.  This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy.  Our framework is applicable to unlabled \emph{mixed-type data}, that may include binary, categorical, and real-valued data.  We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT).  We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.