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Unlocking High-Accuracy Differentially Private Image Classification through Scale. (arXiv:2204.13650v2 [cs.LG] UPDATED)
June 17, 2022, 1:12 a.m. | Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle
stat.ML updates on arXiv.org arxiv.org
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, …
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