Aijack

Latest version: v0.0.1b2

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0.0.1beta.2

What's Changed

* Update gradientinversion_server.py by Koukyosyumei in https://github.com/Koukyosyumei/AIJack/pull/160
* Implement DPlis by Koukyosyumei in https://github.com/Koukyosyumei/AIJack/pull/161
* Implement ModelReplacement by Koukyosyumei in https://github.com/Koukyosyumei/AIJack/pull/163
* implement Model-Contrastive Federated Learning by Koukyosyumei in https://github.com/Koukyosyumei/AIJack/pull/164
* Implement FedExP by Koukyosyumei in https://github.com/Koukyosyumei/AIJack/pull/166

We have also published [a short paper](https://arxiv.org/abs/2312.17667) at arXiv.

**Full Changelog**: https://github.com/Koukyosyumei/AIJack/compare/v0.0.1-beta.1...v0.0.1-beta.2

0.0.1alpha.2

- New documents and examples
- Implement AdaDPS
- Implement K-anonymity
- Refactoring Federated Learning
- Refactoring Membership Inference Attack

v0.0.1-alpha.1-new

0.0.1alpha

Distributed Learning

| | Example | Paper |
| ----------- | ------------------------------------------------- | ----------------------------------------- |
| FedAVG | [example](docs/aijack_fedavg.ipynb) | [paper](https://arxiv.org/abs/1602.05629) |
| FedProx | WIP | [paper](https://arxiv.org/abs/1812.06127) |
| FedKD | [example](test/collaborative/fedkd/test_fedkd.py) | [paper](https://arxiv.org/abs/2108.13323) |
| FedMD | [example](docs/aijack_fedmd.ipynb) | [paper](https://arxiv.org/abs/1910.03581) |
| FedGEMS | WIP | [paper](https://arxiv.org/abs/2110.11027) |
| DSFL | WIP | [paper](https://arxiv.org/abs/2008.06180) |
| SplitNN | [example](docs/aijack_split_learning.ipynb) | [paper](https://arxiv.org/abs/1812.00564) |
| SecureBoost | [example](docs/aijack_secureboost.ipynb) | [paper](https://arxiv.org/abs/1901.08755) |

Attack

| | Attack Type | Example | Paper |
| ------------------------ | -------------------- | --------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| MI-FACE | Model Inversion | [example](docs/aijack_miface.ipynb) | [paper](https://dl.acm.org/doi/pdf/10.1145/2810103.2813677) |
| DLG | Model Inversion | [example](docs/aijack_gradient_inversion_attack.ipynb) | [paper](https://papers.nips.cc/paper/2019/hash/60a6c4002cc7b29142def8871531281a-Abstract.html) |
| iDLG | Model Inversion | [example](docs/aijack_gradient_inversion_attack.ipynb) | [paper](https://arxiv.org/abs/2001.02610) |
| GS | Model Inversion | [example](docs/aijack_gradient_inversion_attack.ipynb) | [paper](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html) |
| CPL | Model Inversion | [example](docs/aijack_gradient_inversion_attack.ipynb) | [paper](https://arxiv.org/abs/2004.10397) |
| GradInversion | Model Inversion | [example](docs/aijack_gradient_inversion_attack.ipynb) | [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.pdf) |
| GAN Attack | Model Inversion | [example](example/model_inversion/gan_attack.py) | [paper](https://arxiv.org/abs/1702.07464) |
| Shadow Attack | Membership Inference | [example](docs/aijack_membership_inference.ipynb) | [paper](https://arxiv.org/abs/1610.05820) |
| Norm attack | Label Leakage | [example](docs/aijack_split_learning.ipynb) | [paper](https://arxiv.org/abs/2102.08504) |
| Delta Weights | Free Rider Attack | WIP | [paper](https://arxiv.org/pdf/1911.12560.pdf) |
| Gradient descent attacks | Evasion Attack | [example](docs/aijack_evasion_attack.ipynb) | [paper](https://arxiv.org/abs/1708.06131) |
| DBA | Backdoor Attack | WIP | [paper](https://openreview.net/forum?id=rkgyS0VFvr) |
| Label Flip Attack | Poisoning Attack | [example](docs/aijack_poisoning_federated_learning.ipynb) | [paper](https://arxiv.org/abs/2203.08669) |
| History Attack | Poisoning Attack | [example](docs/aijack_poisoning_federated_learning.ipynb) | [paper](https://arxiv.org/abs/2203.08669) |
| MAPF | Poisoning Attack | [example](docs/aijack_poisoning_federated_learning.ipynb) | [paper](https://arxiv.org/abs/2203.08669) |
| SVM Poisoning | Poisoning Attack | [example](docs/aijack_poisoning_attack_svm.ipynb) | [paper](https://arxiv.org/abs/1206.6389) |


Defense

| | Defense Type | Example | Paper |
| --------------- | ---------------------- | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| DPSGD | Differential Privacy | [example](docs/aijack_miface.ipynb) | [paper](https://arxiv.org/abs/1607.00133) |
| Paillier | Homomorphic Encryption | [example](docs/aijack_secureboost.ipynb) | [paper](https://link.springer.com/chapter/10.1007/3-540-48910-X_16) | |
| CKKS | Homomorphic Encryption | [test](test/defense/ckks/test_core.py) | [paper](https://eprint.iacr.org/2016/421.pdf) | |
| Soteria | Others | [example](docs/aijack_soteria.ipynb) | [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.pdf) |
| FoolsGold | Others | WIP | [paper](https://arxiv.org/abs/1808.04866) |
| Sparse Gradient | Others | [example](docs/aijack_fedavg.ipynb) | [paper](https://aclanthology.org/D17-1045/) |
| MID | Others | [example](docs/aijack_mid.ipynb) | [paper](https://arxiv.org/abs/2009.05241) |

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