Unlocking High-Accuracy Differentially Private Image Classification through Scale. (arXiv:2204.13650v2 [cs.LG] UPDATED)

Differential Privacy (DP) provides a formal privacy guarantee preventing
adversaries with access to a machine learning model from extracting information
about individual training points. Differentially Private Stochastic Gradient
Descent (DP-SGD), the most popular DP training method for deep learning,
realizes this protection by injecting noise during training. However previous
works have found that DP-SGD often leads to a significant degradation in
performance on standard image classification benchmarks. Furthermore, some
authors have postulated that DP-SGD inherently performs poorly on large models, …

classification lg scale