Bridging the Privacy Accounting Gap in DP-SGD

Abstract

Differentially Private Stochastic Gradient Descent (DP-SGD) is one of the most widely used algorithms for private machine learning. Due to its efficiency, most practical implementations of DP-SGD shuffle the training examples and divide them into fixed-size mini-batches during training. However, the privacy accounting typically assumes that Poisson subsampling was used, wherein each example is included in each mini-batch independently with some probability. Our first contribution is to show that there can be a substantial gap between these two versions of DP-SGD; specifically, the privacy accounting implies much stronger privacy guarantees than the implementations actually provide. As our second contribution, we propose two approaches to address this gap: (i) an implementation of Poisson subsampling using the Map-Reduce framework that can scale to large datasets that do not fit in memory and (ii) a novel Balls-and-Bins sampling that achieves the “best of both” sampling approaches. Namely, its implementation is similar to shuffling, and it leads to similar utility for DP-SGD training with similar-or-better privacy compared to Poisson subsampling in practical regimes of parameters.