Deep Learning with Gaussian Differential Privacy

Abstract

Deep learning models are often trained on data sets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses in handling certain important primitives (composition and subsampling), thereby giving loose or complicated privacy analyses of training neural networks. In this article, we consider a recently proposed privacy definition termed f -differential privacy Leveraging the appealing properties of f -differential privacy in handling composition and subsampling, this article derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as Abadi et al. ( Our results demonstrate that the f -differential privacy framework allows for a new privacy analysis that improves on the prior analysis These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems. Python code to calculate the privacy cost for these experiments is publicly available in the TensorFlow Privacy library.

Topics & Concepts