Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Abstract Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private deep networks often lag far behind their non-private counterparts in accuracy, showing the need for more research in model architectures, optimizers, etc. One of the …