{ "info": { "author": "Hideaki Takahashi", "author_email": "", "bugtrack_url": null, "classifiers": [], "description": "\n\n# AIJack: Security and Privacy Risk Simulator for Machine Learning\n\n
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\n\n# What is AIJack?\n\nAIJack is an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers. It provides advanced security techniques like *Differential Privacy*, *Homomorphic Encryption*, *K-anonymity* and *Federated Learning* to guarantee protection for your AI. With AIJack, you can test and simulate defenses against various attacks such as *Poisoning*, *Model Inversion*, *Backdoor*, and *Free-Rider*. We support more than 30 state-of-the-art methods. For more information, check our [documentation](https://koukyosyumei.github.io/AIJack/) and start securing your AI today with AIJack.\n\n# Installation\n\nYou can install AIJack with `pip`. AIJack requires Boost and pybind11.\n\n```\napt install -y libboost-all-dev\npip install -U pip\npip install \"pybind11[global]\"\n\npip install aijack\n```\n\nIf you want to use the latest-version, you can directly install from GitHub.\n\n```\npip install git+https://github.com/Koukyosyumei/AIJack\n```\n\nWe also provide [Dockerfile](Dockerfile).\n\n\n# Quick Start\n\nWe briefly introduce the overview of AIJack.\n\n## Features\n\n- All-around abilities for both attack & defense\n- PyTorch-friendly design\n- Compatible with scikit-learn\n- Fast Implementation with C++ backend\n- MPI-Backend for Federated Learning\n- Extensible modular APIs\n\n## Basic Interface\n\n### Python API\n\nFor standard machine learning algorithms, AIJack allows you to simulate attacks against machine learning models with `Attacker` APIs. AIJack mainly supports PyTorch or sklearn models.\n\n```Python\n# abstract code\n\nattacker = Attacker(target_model)\nresult = attacker.attack()\n```\n\nFor instance, we can implement Poisoning Attack against SVM implemented with sklearn as follows.\n\n```Python\nfrom aijack . attack import Poison_attack_sklearn\n\nattacker = Poison_attack_sklearn (clf , X_train , y_train)\nmalicious_data , log = attacker.attack(initial_data , 1, X_valid , y_valid)\n```\n\nFor distributed learning such as Federated Learning and Split Learning, AIJack offers four basic APIs: `Client`, `Server`, `API`, and `Manager`. `Client` and `Server` represent each client and server within each distributed learning scheme. You can execute training by registering the clients and servers to `API` and running it. `Manager` gives additional abilities such as attack, defense, or parallel computing to `Client`, `Server` or `API` via `attach` method.\n\n```Python\n# abstract code\n\nclient = [Client(), Client()]\nserver = Server()\napi = API(client, server)\napi.run() # execute training\n\nc_manager = ClientManagerForAdditionalAbility(...)\ns_manager = ServerManagerForAdditionalAbility(...)\nExtendedClient = c_manager.attach(Client)\nExtendedServer = c_manager.attach(Server)\n\nextended_client = [ExtendedClient(...), ExtendedClient(...)]\nextended_server = ExtendedServer(...)\napi = API(extended_client, extended_server)\napi.run() # execute training\n```\n\nFor example, the bellow code implements the scenario where the server in Federated Learning tries to steal the training data with gradient-based model inversion attack.\n\n```Python\nfrom aijack.collaborative.fedavg import FedAVGAPI, FedAVGClient, FedAVGServer\nfrom aijack.attack.inversion import GradientInversionAttackServerManager\n\nmanager = GradientInversionAttackServerManager(input_shape)\nFedAVGServerAttacker = manager.attach(FedAVGServer)\n\nclients = [FedAVGClient(model_1), FedAVGClient(model_2)]\nserver = FedAVGServerAttacker(clients, model_3)\n\napi = FedAVGAPI(server, clients, criterion, optimizers, dataloaders)\napi.run()\n```\n\n### AIValut: A simple DBMS for debugging ML Models\n\nWe also provide a simple DBMS named `AIValut` designed specifically for SQL-based algorithms. AIValut currently supports Rain, a SQL-based debugging system for ML models. In the future, we have plans to integrate additional advanced features from AIJack, including K-Anonymity, Homomorphic Encryption, and Differential Privacy. \n\nAIValut has its own storage engine and query parser, and you can train and debug ML models with SQL-like queries. For example, the `Complaint` query automatically removes problematic records given the specified constraint.\n\n```sql\n# We train an ML model to classify whether each customer will go bankrupt or not based on their age and debt.\n# We want the trained model to classify the customer as positive when he/she has more debt than or equal to 100.\n# The 10th record seems problematic for the above constraint.\n>>Select * From bankrupt\nid age debt y\n1 40 0 0\n2 21 10 0\n3 22 10 0\n4 32 30 0\n5 44 50 1\n6 30 100 1\n7 63 310 1\n8 53 420 1\n9 39 530 1\n10 49 1000 0\n\n# Train Logistic Regression with the number of iterations of 100 and the learning rate of 1.\n# The name of the target feature is `y`, and we use all other features as training data.\n>>Logreg lrmodel id y 100 1 From Select * From bankrupt\nTrained Parameters:\n (0) : 2.771564\n (1) : -0.236504\n (2) : 0.967139\nAUC: 0.520000\nPrediction on the training data is stored at `prediction_on_training_data_lrmodel`\n\n# Remove one record so that the model will predict `positive (class 1)` for the samples with `debt` greater or equal to 100.\n>>Complaint comp Shouldbe 1 Remove 1 Against Logreg lrmodel id y 100 1 From Select * From bankrupt Where debt Geq 100\nFixed Parameters:\n (0) : -4.765492\n (1) : 8.747224\n (2) : 0.744146\nAUC: 1.000000\nPrediction on the fixed training data is stored at `prediction_on_training_data_comp_lrmodel`\n```\n\nFor more detailed information and usage instructions, please refer to [aivalut/README.md](aivalut/README.md).\n\n> Please use AIValut only for research purpose. \n\n## Resources\n\nYou can also find more examples in our tutorials and documentation.\n\n- [Examples](docs/source/notebooks)\n- [Documentation](https://koukyosyumei.github.io/AIJack/)\n- [API Reference](https://koukyosyumei.github.io/AIJack/api.html)\n\n# Supported Algorithms\n\n| | | |\n| ------------- | ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Collaborative | Horizontal FL | [FedAVG](https://arxiv.org/abs/1602.05629), [FedProx](https://arxiv.org/abs/1812.06127), [FedKD](https://arxiv.org/abs/2108.13323), [FedGEMS](https://arxiv.org/abs/2110.11027), [FedMD](https://arxiv.org/abs/1910.03581), [DSFL](https://arxiv.org/abs/2008.06180), [MOON](https://arxiv.org/abs/2103.16257), [FedExP](https://arxiv.org/abs/2301.09604) |\n| Collaborative | Vertical FL | [SplitNN](https://arxiv.org/abs/1812.00564), [SecureBoost](https://arxiv.org/abs/1901.08755) |\n| Attack | Model Inversion | [MI-FACE](https://dl.acm.org/doi/pdf/10.1145/2810103.2813677), [DLG](https://papers.nips.cc/paper/2019/hash/60a6c4002cc7b29142def8871531281a-Abstract.html), [iDLG](https://arxiv.org/abs/2001.02610), [GS](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html), [CPL](https://arxiv.org/abs/2004.10397), [GradInversion](https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.pdf), [GAN Attack](https://arxiv.org/abs/1702.07464) |\n| Attack | Label Leakage | [Norm Attack](https://arxiv.org/abs/2102.08504) |\n| Attack | Poisoning | [History Attack](https://arxiv.org/abs/2203.08669), [Label Flip](https://arxiv.org/abs/2203.08669), [MAPF](https://arxiv.org/abs/2203.08669), [SVM Poisoning](https://arxiv.org/abs/1206.6389) |\n| Attack | Backdoor | [DBA](https://openreview.net/forum?id=rkgyS0VFvr), [Model Replacement](https://proceedings.mlr.press/v108/bagdasaryan20a.html) |\n| Attack | Free-Rider | [Delta-Weight](https://arxiv.org/pdf/1911.12560.pdf) |\n| Attack | Evasion | [Gradient-Descent Attack](https://arxiv.org/abs/1708.06131), [FGSM](https://arxiv.org/abs/1412.6572), [DIVA](https://arxiv.org/abs/2204.10933) |\n| Attack | Membership Inference | [Shadow Attack](https://arxiv.org/abs/1610.05820) |\n| Defense | Homomorphic Encryption | [Paillier](https://link.springer.com/chapter/10.1007/3-540-48910-X_16) |\n| Defense | Differential Privacy | [DPSGD](https://arxiv.org/abs/1607.00133), [AdaDPS](https://arxiv.org/pdf/2202.05963.pdf), [DPlis](https://arxiv.org/pdf/2103.01496.pdf) |\n| Defense | Anonymization | [Mondrian](https://ieeexplore.ieee.org/document/1617393) |\n| Defense | Robust Training | [PixelDP](https://arxiv.org/abs/1802.03471v4), [Cost-Aware Robust Tree Ensemble](https://arxiv.org/abs/1912.01149) |\n| Defense | Debugging | [Model Assertions](https://cs.stanford.edu/~matei/papers/2019/debugml_model_assertions.pdf), [Rain](https://arxiv.org/abs/2004.05722), [Neuron Coverage](https://dl.acm.org/doi/abs/10.1145/3132747.3132785) |\n| Defense | Others | [Soteria](https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.pdf), [FoolsGold](https://arxiv.org/abs/1808.04866), [MID](https://arxiv.org/abs/2009.05241), [Sparse Gradient](https://aclanthology.org/D17-1045/) |\n\n-----------------------------------------------------------------------\n\n# Citation\n\nIf you use AIJack for your research, please cite the repo and our arXiv paper.\n\n```\n@misc{repotakahashi2023aijack,\n author = {Hideaki, Takahashi},\n title = {AIJack},\n year = {2023},\n publisher = {GitHub},\n journal = {GitHub Repository},\n howpublished = {\\url{https://github.com/Koukyosyumei/AIJack}},\n}\n\n@misc{takahashi2023aijack,\n title={AIJack: Security and Privacy Risk Simulator for Machine Learning}, \n author={Hideaki Takahashi},\n year={2023},\n eprint={2312.17667},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n# Related Publications\n\nBelow you can find a list of papers and books that either use or extend AIJack.\n\n- Huang, Shiyuan, et al. \"Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022.\n- Song, Junzhe, and Dmitry Namiot. \"A Survey of the Implementations of Model Inversion Attacks.\" International Conference on Distributed Computer and Communication Networks. Cham: Springer Nature Switzerland, 2022.\n- Kapoor, Amita, and Sharmistha Chatterjee. Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models. Packt Publishing Ltd, 2023.\n- Mi, Yuxi, et al. \"Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings.\" arXiv preprint arXiv:2308.02362 (2023).\n- Mohammadi, Mohammadreza, et al. \"Privacy-preserving Federated Learning System for Fatigue Detection.\" 2023 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2023.\n\n# Contact\n\nwelcome2aijack[@]gmail.com\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "", "keywords": "", "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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