Deep Learning with Differential Privacy

Abstract

The authors develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy to train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.