PyVacy: Towards Practical Differential Privacy for Deep Learning

Abstract

In this work, we present an extension to the PyTorch deep learning framework which facilitates differentially private optimization. We discuss the algorithms provided by the extension and compare its contribution to that of other libraries in existence. We then go on to demonstrate the performance of the resulting private models on several statistical tasks, as well as the incurred overhead and structural changes of the resulting scripts. We find that our extension enables the construction of models which retain practical performance while incurring a minimal impact on existing, non-private training procedures.