GitHub - pytorch/opacus: Training PyTorch models with differential privacy

Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. To train your model with differential privacy, all you need to do is to instantiate a PrivacyEngine and pass your model, data_loader, and optimizer to the engine's make_private() method to obtain their private counterparts. The MNIST example shows an end-to-end run using Opacus. The examples folder contains more such examples.