1## Differential Privacy with ResNet18 2 3### Differential Privacy 4Differential privacy is a way of training models that ensures no attacker can figure out the training 5data from the gradient updates of the model. Recently, a paper was published comparing the performance of 6Opacus to a JAX-based system. 7 8[Original differential privacy paper](https://people.csail.mit.edu/asmith/PS/sensitivity-tcc-final.pdf) 9[JAX-based differential privacy paper](https://arxiv.org/pdf/2010.09063.pdf) 10 11### Opacus 12Opacus is a differential privacy library built for PyTorch. They have added hooks to PyTorch's 13autograd that compute per sample gradients and a differential privacy engine that computes 14differentially private weight updates. 15 16### Example 17This example runs ResNet18 by either having Opacus compute the differentially private updates or 18getting the per sample gradients using vmap and grad and computing the differentially private update 19from those. 20 21As a caveat, the transforms version may not be computing the exact same values as the opacus version. 22No verification has been done yet for this. 23 24### Requirements 25These examples use Opacus version 1.0.1 and torchvision 0.11.2 26