可扩展性
向量化逐样本梯度计算，比微批次（microbatching）快 10 倍
基于 PyTorch 构建
支持大多数类型的 PyTorch 模型，只需对原始神经网络进行极小的修改即可使用。
可扩展性
开源、模块化的差分隐私研究 API。欢迎所有人贡献代码。
pip install opacus
conda install -c conda-forge opacus
git clone https://github.com/pytorch/opacus.git
cd opacus
pip install -e .
# define your components as usual
model = Net()
optimizer = SGD(model.parameters(), lr=0.05)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=1024)
# enter PrivacyEngine
privacy_engine = PrivacyEngine()
model, optimizer, data_loader = privacy_engine.make_private(
module=model,
optimizer=optimizer,
data_loader=data_loader,
noise_multiplier=1.1,
max_grad_norm=1.0,
)
# Now it's business as usual