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Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method for deep learning, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA without extra data on CIFAR-10 of 81. 4% under (8, 10^-5) -DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71. 7%. When fine-tuning a pre-trained NFNet-F3, we achieve a remarkable 83. 8% top-1 accuracy on ImageNet under (0. 5, 8*10^-7) -DP. Additionally, we also achieve 86. 7% top-1 accuracy under (8, 8 10^-7) -DP, which is just 4. 3% below the current non-private SOTA for this task. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification.
De et al. (Thu,) studied this question.