DPWatch: A Framework for Hardware-Based Differential Privacy Guarantees

Abstract

Differential privacy (DP) and federated learning (FL) have emerged as important privacy-preserving approaches when using sensitive data to train machine learning models. FL ensures that raw sensitive data does not leave the users’ devices by training the model in a distributed manner. DP ensures that the model does not leak any information about an individual by clipping and adding noise to the gradients. However, real-life deployments of such algorithms assume that the third-party application implementing DP-based FL is trusted, and is thus given access to sensitive data on the data owner’s device/server. In this work, we propose DPWatch, a hardware-based framework for ML accelerators that enforces guarantees that a third party application cannot leak sensitive user data used for training and ensures that the gradients are appropriately noised before leaving the device. We evaluate DPWatch on two accelerators and demonstrate small area and performance overheads.