https://github.com/koukyosyumei/attack_splitnn

reveal the vulnerabilities of SplitNN
https://github.com/koukyosyumei/attack_splitnn

attack attack-splitnn leakage membership-inference membership-inference-attack model-inversion model-inversion-attacks notebook paper privacy security-vulnerability split-learning splitnn

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reveal the vulnerabilities of SplitNN

• Host: GitHub
• URL: https://github.com/koukyosyumei/attack_splitnn
• Owner: Koukyosyumei
• License: mit
• Created: 2021-03-21T06:59:36.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-06-16T14:08:41.000Z (over 2 years ago)
• Last Synced: 2024-05-02T03:39:01.048Z (7 months ago)
• Topics: attack, attack-splitnn, leakage, membership-inference, membership-inference-attack, model-inversion, model-inversion-attacks, notebook, paper, privacy, security-vulnerability, split-learning, splitnn
• Language: Python
• Homepage:
• Size: 58 MB
• Stars: 27
• Watchers: 2
• Forks: 6
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# 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](examples/IntermidiateLevelAttack.ipynb)     | [original paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_Enhancing_Adversarial_Example_Transferability_With_an_Intermediate_Level_Attack_ICCV_2019_paper.pdf) |

| Norm Attack                      | label leakage attack        | [notebook](examples/Label_Leakage.ipynb)               | [original paper](https://arxiv.org/abs/2102.08504)                                                                                                                                 |

| Transfer Inherit Attack          | membership inference attack | [notebook](examples/Membershio_Inference_Attack.ipynb) | [original paper](https://ieeexplore.ieee.org/document/9302683)                                                                                                                     |

|                                  |

| Black Box Model Inversion Attack | model inversion attack      | [notebook](examples/Black_Box_Model_Inversion.ipynb)   | [blog](https://blog.openmined.org/extracting-private-data-from-a-neural-network/)                                                                                                  |

## Defense

|          | example                                  | Reference                                                            |

| -------- | ---------------------------------------- | -------------------------------------------------------------------- |

| Max Norm | [notebook](examples/Label_Leakage.ipynb) | [original paper](https://arxiv.org/abs/2102.08504)                   |

| NoPeek   | [notebook](examples/NoPeekLoss.ipynb)    | [original paper](https://arxiv.org/abs/2008.09161)                   |

| Shredder | [notebook](examples/Shredder.ipynb)      | [original paper](https://dl.acm.org/doi/pdf/10.1145/3373376.3378522) |

## License

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