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reveal the vulnerabilities of SplitNN
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Koukyosyumei/Attack_SplitNN
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Attacking_SplitNN allows you to easily experiment with various combinations of attack and defense algorithms against SplitNN within PyTorch and scikit-learn.
pip install git+https://github.com/Koukyosyumei/Attack_SplitNNYou can easily create two-SplitNN with this package as follows.
The client only has input data, and the server has only labels.This package implements SplitNN as the custom torch.nn.modules, so youcan train SplitNN like the normal torch models.
Examples: model_1 = FirstNet() model_1 = model_1.to(device) model_2 = SecondNet() model_2 = model_2.to(device) opt_1 = optim.Adam(model_1.parameters(), lr=1e-3) opt_2 = optim.Adam(model_2.parameters(), lr=1e-3) criterion = nn.BCELoss() client = Client(model_1) server = Server(model_2) splitnn = SplitNN(client, server, opt_1, opt_2) splitnn.train() for epoch in range(3): epoch_loss = 0 epoch_outputs = [] epoch_labels = [] for i, data in enumerate(train_loader): splitnn.zero_grads() inputs, labels = data inputs = inputs.to(device) labels = labels.to(device) outputs = splitnn(inputs) loss = criterion(outputs, labels) loss.backward() epoch_loss += loss.item() / len(train_loader.dataset) epoch_outputs.append(outputs) epoch_labels.append(labels) splitnn.backward() splitnn.step() print(epoch_loss, torch_auc(torch.cat(epoch_labels), torch.cat(epoch_outputs)))Attacking_SplitNN offers several attack methods with the same interface.
| type | example | Reference | |
|---|---|---|---|
| Intermidiate Level Attack | evasion attack | notebook | original paper |
| Norm Attack | label leakage attack | notebook | original paper |
| Transfer Inherit Attack | membership inference attack | notebook | original paper |
| Black Box Model Inversion Attack | model inversion attack | notebook | blog |
| example | Reference | |
|---|---|---|
| Max Norm | notebook | original paper |
| NoPeek | notebook | original paper |
| Shredder | notebook | original paper |
This software is released under the MIT License, see LICENSE.txt.
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