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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: about 23 hours 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 (about 2 years ago)
Default Branch: main
Last Pushed: 2024-04-28T02:40:46.000Z (about 2 months ago)
Last Synced: 2024-04-28T04:49:43.989Z (about 2 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: 191 MB
Stars: 2,193
Watchers: 52
Forks: 360
Open Issues: 136
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md

Lists

Awesome-SGX-Open-Source - https://github.com/secretflow/secretflow
awesome - secretflow/secretflow - A unified framework for privacy-preserving data analysis and machine learning (Python)
Awesome-FL - SecretFlow - commit/secretflow/secretflow) | | [Ant group](https://www.antgroup.com/) | | :white_check_mark: | [[DOC](https://secretflow.readthedocs.io/en/latest/getting_started/index.html)] | (federated learning framework / table)
awesome-stars - secretflow/secretflow - A unified framework for privacy-preserving data analysis and machine learning (Python)
FLsystem-paper - `Github`
awesome-identity - Secretflow - preserving data intelligence and machine learning (MPC-based Solutions / General MPC Frameworks)
awesome-privacy-engineering - SecretFlow - SecretFlow is a unified framework for privacy-preserving data analysis and machine learning. (Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) / Machine Learning and Algorithmic Bias)
awesome-stars - secretflow/secretflow - `★2235` A unified framework for privacy-preserving data analysis and machine learning (Python)

README

        


    



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简体中文｜English



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

Please check [CONTRIBUTING.md](./CONTRIBUTING.md)

## 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.