Attacking SplitNN

Attacking_SplitNN allows you to easily experiment with various combinations of attack and defense algorithms against SplitNN within PyTorch and scikit-learn.

Install

    pip install git+https://github.com/Koukyosyumei/Attack_SplitNN

SplitNN

You 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 you can 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)))

Attack

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

Defense

	example	Reference
Max Norm	notebook	original paper
NoPeek	notebook	original paper
Shredder	notebook	original paper

License

This software is released under the MIT License, see LICENSE.txt.