Learning often involves sensitive data and as such, privacy preserving
extensions to Stochastic Gradient Descent (SGD) and other machine learning
algorithms have been developed using the definitions of Differential Privacy
(DP). In differentially private SGD, the gradients computed at each training
iteration are subject to two different types of noise. Firstly, inherent
sampling noise arising from the use of minibatches. Secondly, additive Gaussian
noise from the underlying mechanisms that introduce privacy. In this study, we
show that these two types of noise are equivalent in their effect on the
utility of private neural networks, however they are not accounted for equally
in the privacy budget. Given this observation, we propose a training paradigm
that shifts the proportions of noise towards less inherent and more additive
noise, such that more of the overall noise can be accounted for in the privacy
budget. With this paradigm, we are able to improve on the state-of-the-art in
the privacy/utility tradeoff of private end-to-end CNNs.

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