  Training deep neural networks (DNNs) for meaningful differential privacy (DP)
guarantees severely degrades model utility. In this paper, we demonstrate that
the architecture of DNNs has a significant impact on model utility in the
context of private deep learning, whereas its effect is largely unexplored in
previous studies. In light of this missing, we propose the very first framework
that employs neural architecture search to automatic model design for private
deep learning, dubbed as DPNAS. To integrate private learning with architecture
search, we delicately design a novel search space and propose a DP-aware method
for training candidate models. We empirically certify the effectiveness of the
proposed framework. The searched model DPNASNet achieves state-of-the-art
privacy/utility trade-offs, e.g., for the privacy budget of $(\epsilon,
\delta)=(3, 1\times10^{-5})$, our model obtains test accuracy of $98.57\%$ on
MNIST, $88.09\%$ on FashionMNIST, and $68.33\%$ on CIFAR-10. Furthermore, by
studying the generated architectures, we provide several intriguing findings of
designing private-learning-friendly DNNs, which can shed new light on model
design for deep learning with differential privacy.
