https://github.com/secretflow/secretflow
A unified framework for privacy-preserving data analysis and machine learning
https://github.com/secretflow/secretflow
confidential-computing data-analysis differential-privacy federated-learning homomorphic-encryption machine-learning privacy-preserving private-set-intersection secure-multiparty-computation split-learning trusted-execution-environment
Last synced: 10 days ago
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A unified framework for privacy-preserving data analysis and machine learning
- Host: GitHub
- URL: https://github.com/secretflow/secretflow
- Owner: secretflow
- License: apache-2.0
- Created: 2022-04-15T09:07:44.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-24T07:22:30.000Z (4 months ago)
- Last Synced: 2025-01-06T03:31:23.169Z (3 months ago)
- Topics: confidential-computing, data-analysis, differential-privacy, federated-learning, homomorphic-encryption, machine-learning, privacy-preserving, private-set-intersection, secure-multiparty-computation, split-learning, trusted-execution-environment
- Language: Python
- Homepage: https://www.secretflow.org.cn/docs/secretflow/en/
- Size: 204 MB
- Stars: 2,376
- Watchers: 51
- Forks: 411
- Open Issues: 137
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
- awesome-privacy-engineering - SecretFlow - SecretFlow is a unified framework for privacy-preserving data analysis and machine learning. (Awesome Privacy Engineering [](https://awesome.re) / Machine Learning and Algorithmic Bias)
- Awesome-SGX-Open-Source - https://github.com/secretflow/secretflow
- awesome-identity - Secretflow - preserving data intelligence and machine learning (MPC-based Solutions / General MPC Frameworks)
- FLsystem-paper - `Github`
README
---
[](https://dl.circleci.com/status-badge/redirect/gh/secretflow/secretflow/tree/main)
[](https://github.com/orgs/secretflow/projects/12/)
[](https://github.com/orgs/secretflow/projects/11/)
SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. To achieve this goal,
it provides:
- An abstract device layer consists of plain devices and secret devices which encapsulate various cryptographic protocols.
- A device flow layer modeling higher algorithms as device object flow and DAG.
- An algorithm layer to do data analysis and machine learning with horizontal or vertical partitioned data.
- A workflow layer that seamlessly integrates data processing, model training, and hyperparameter tuning.
## Documentation
- [SecretFlow](https://www.secretflow.org.cn/docs/secretflow/en/)
- [Getting Started](https://www.secretflow.org.cn/docs/secretflow/en/getting_started/index.html)
- [User Guide](https://www.secretflow.org.cn/docs/secretflow/en/user_guide/index.html)
- [API Reference](https://www.secretflow.org.cn/docs/secretflow/en/api/index.html)
- [Tutorial](https://www.secretflow.org.cn/docs/secretflow/en/tutorial/index.html)
## SecretFlow Related Projects
- [Kuscia](https://github.com/secretflow/kuscia): A lightweight privacy-preserving computing task orchestration framework based on K3s.
- [SCQL](https://github.com/secretflow/scql): A system that allows multiple distrusting parties to run joint analysis without revealing their private data.
- [SPU](https://github.com/secretflow/spu): A provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
- [HEU](https://github.com/secretflow/heu): A high-performance homomorphic encryption algorithm library.
- [YACL](https://github.com/secretflow/yacl): A C++ library that contains cryptography, network and io modules which other SecretFlow code depends on.
## Install
Please check [INSTALLATION.md](./docs/getting_started/installation.md)
## Deployment
Please check [DEPLOYMENT.md](./docs/getting_started/deployment.md)
## Learn PETs
We also provide a curated list of papers and SecretFlow's tutorials on Privacy-Enhancing Technologies (PETs).
Please check [AWESOME-PETS.md](./docs/awesome-pets/awesome-pets.md)
## Contributing
- Contributor Rewards: Thank you for contributing to SecretFlow! All contributors will receive: A SecretFlow Open Source Contributor Certificate & An exclusive SecretFlow T-shirt 📌 [Apply Now](https://studio.secretflow.com/activity/fhelc1w2nmx0g0n/detail)
### Good First Issues
We have a list of [good first issues](https://github.com/orgs/secretflow/projects/12/). This is a great place for newcomers and beginners alike to get started, gain experience, and get familiar with our contribution process.
### Contribution Map
We also welcome community collaboration on [more advanced initiatives](https://github.com/orgs/secretflow/projects/11/)! Whether you're refining features, optimizing workflows, or proposing new ideas – there are opportunities for contributors of all skill levels to shape SecretFlow's future.
## Benchmarks
Please check [OVERALL_BENCHMARK.md](./docs/developer/benchmark/overall_benchmark.md)
## Disclaimer
Non-release versions of SecretFlow are prohibited from using in any production environment due to possible bugs, glitches, lack of functionality, security issues or other problems.