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In privacy-preserving machine learning, differentially private stochasticgradient descent (DP-SGD) performs worse than SGD due to per-sample gradientclipping and noise addition. A recent focus in private learning research isimproving the performance of DP-SGD on private data by incorporating priorsthat are learned on real-world public data. In this work, we explore how we canimprove the privacy-utility tradeoff of DP-SGD by learning priors from imagesgenerated by random processes and transferring these priors to private data. Wepropose DP-RandP, a three-phase approach. We attain new state-of-the-artaccuracy when training from scratch on CIFAR10, CIFAR100, MedMNIST and ImageNetfor a range of privacy budgets $\varepsilon \in [1, 8]$. In particular, weimprove the previous best reported accuracy on CIFAR10 from $60.6 \%$ to $72.3\%$ for $\varepsilon=1$.

Differentially Private Image Classification by Learning Priors from Random Processes