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cs.LG2021★ 1 cited
DP-FP: Differentially Private Forward Propagation for Large Models
Jian Du, Haitao Mi
When applied to large-scale learning problems, the conventional wisdom on privacy-preserving deep learning, known as Differential Private Stochastic Gradient Descent (DP-SGD), has…
cs.LG2021★ 7 cited
Dynamic Differential-Privacy Preserving SGD
Jian Du, Song Li, Xiangyi Chen +2
The vanilla Differentially-Private Stochastic Gradient Descent (DP-SGD), including DP-Adam and other variants, ensures the privacy of training data by uniformly distributing privac…