WhenDoesDifferentiallyPrivateLearning NotSufferinHighDimensions?

Large pretrained models can be fine-tuned with differential privacy to achieve performance approaching thatofnon-privatemodels. Acommon themeinthese results is the surprising observation that high-dimensional models can achieve favorable privacy-utility trade-offs.