Poster

Fast and Memory Efficient Differentially Private-SGD via JL Projections

Zhiqi Bu · Sivakanth Gopi · Janardhan Kulkarni · Yin Tat Lee · Hanwen Shen · Uthaipon Tantipongpipat

Keywords: [ Deep Learning ] [ Privacy ]

[ Abstract ]
Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST

Abstract:

Differentially Private-SGD (DP-SGD) of Abadi et al. and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requires computation of per-sample gradients norms which is extremely slow and memory intensive in practice. In this paper, we present a new framework to design differentially private optimizers called DP-SGD-JL and DP-Adam-JL. Our approach uses Johnson–Lindenstrauss (JL) projections to quickly approximate the per-sample gradient norms without exactly computing them, thus making the training time and memory requirements of our optimizers closer to that of their non-DP versions. Unlike previous attempts to make DP-SGD faster which work only on a subset of network architectures or use compiler techniques, we propose an algorithmic solution which works for any network in a black-box manner which is the main contribution of this paper. To illustrate this, on IMDb dataset, we train a Recurrent Neural Network (RNN) to achieve good privacy-vs-accuracy tradeoff, while being significantly faster than DP-SGD and with a similar memory footprint as non-private SGD.

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