  We demonstrate that differentially private machine learning has not yet
reached its "AlexNet moment" on many canonical vision tasks: linear models
trained on handcrafted features significantly outperform end-to-end deep neural
networks for moderate privacy budgets. To exceed the performance of handcrafted
features, we show that private learning requires either much more private data,
or access to features learned on public data from a similar domain. Our work
introduces simple yet strong baselines for differentially private learning that
can inform the evaluation of future progress in this area.
