1 research outputs found
On the effect of normalization layers on Differentially Private training of deep Neural networks
Differentially private stochastic gradient descent (DPSGD) is a variation of
stochastic gradient descent based on the Differential Privacy (DP) paradigm,
which can mitigate privacy threats that arise from the presence of sensitive
information in training data. However, one major drawback of training deep
neural networks with DPSGD is a reduction in the models accuracy. In this
paper, we study the effect of normalization layers on the performance of DPSGD.
We demonstrate that normalization layers significantly impact the utility of
deep neural networks with noisy parameters and should be considered essential
ingredients of training with DPSGD. In particular, we propose a novel method
for integrating batch normalization with DPSGD without incurring an additional
privacy loss. With our approach, we are able to train deeper networks and
achieve a better utility-privacy trade-off