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Differentially private stochastic gradient descent (DPSGD) is a variation ofstochastic gradient descent based on the Differential Privacy (DP) paradigm,which can mitigate privacy threats that arise from the presence of sensitiveinformation in training data. However, one major drawback of training deepneural networks with DPSGD is a reduction in the models accuracy. In thispaper, we study the effect of normalization layers on the performance of DPSGD.We demonstrate that normalization layers significantly impact the utility ofdeep neural networks with noisy parameters and should be considered essentialingredients of training with DPSGD. In particular, we propose a novel methodfor integrating batch normalization with DPSGD without incurring an additionalprivacy loss. With our approach, we are able to train deeper networks andachieve a better utility-privacy trade-off.

On the effect of normalization layers on Differentially Private training of deep Neural networks