Pre-release
This release is a pre-release and may not be stable for production use.
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 Related Projects
- Kuscia: A K8s-based privacy-preserving computing task orchestration framework.
- SCQL: A system that allows multiple distrusting parties to run joint analysis without revealing their private data.
- SPU: A provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
- HEU: A high-performance homomorphic encryption algorithm library.
- YACL: A C++ library that contains cryptography, network and io modules which other SecretFlow code depends on.
Install
Please check INSTALLATION.md
Deployment
Please check 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
Contributing
Please check CONTRIBUTING.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.
1 maintainer
Metadata
1 maintainer
Release files for secretflow 1.2.0.dev20230918
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| secretflow-1.2.0.dev20230918-cp38-cp38-manylinux2014_x86_64.whl | CPython 3.8 | CPython 3.8 | Linux glibc 2.17+ x86-64 | Details |
| secretflow-1.2.0.dev20230918-cp38-cp38-macosx_11_0_arm64.whl | CPython 3.8 | CPython 3.8 | macOS 11.0+ ARM64 | Details |
| secretflow-1.2.0.dev20230918-cp38-cp38-macosx_10_16_x86_64.whl | CPython 3.8 | CPython 3.8 | macOS 10.16+ x86-64 | Details |
Total release size: 4.9 MB
Release files / secretflow-1.2.0.dev20230918-cp38-cp38-manylinux2014_x86_64.whl
| Download URL | secretflow-1.2.0.dev20230918-cp38-cp38-manylinux2014_x86_64.whl |
|---|---|
| Size | 1.4 MB |
| Tags | CPython 3.8 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
73aacd3bf865fa95243914e58d6ac5cefea26118a341b76eba69d67eee846993
|
|
BLAKE2b-256 checksum How to use checksums |
3daf50ee8deff14e7d527422bb0ff07c5a175a2cbb8f2b0a842660687d1d8456
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.18
|
Release files / secretflow-1.2.0.dev20230918-cp38-cp38-macosx_11_0_arm64.whl
| Download URL | secretflow-1.2.0.dev20230918-cp38-cp38-macosx_11_0_arm64.whl |
|---|---|
| Size | 1.7 MB |
| Tags | CPython 3.8 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
155faec6bd4374a7da5341cb26e67cf706baaf76e6e3de59bfaff77c0d0d47ea
|
|
BLAKE2b-256 checksum How to use checksums |
b5d2bf5c8caabec00c1de20ca5fb3542b724244264ec3fdea54d5d4a3d187ec2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.18
|
Release files / secretflow-1.2.0.dev20230918-cp38-cp38-macosx_10_16_x86_64.whl
| Download URL | secretflow-1.2.0.dev20230918-cp38-cp38-macosx_10_16_x86_64.whl |
|---|---|
| Size | 1.8 MB |
| Tags | CPython 3.8 macOS 10.16+ x86-64 |
|
SHA-256 checksum How to use checksums |
8b48854102975c44ad6d4bc5418949a46df99e0c73bca49bef482bd76ce13cd0
|
|
BLAKE2b-256 checksum How to use checksums |
a1f560d3968b1e7345d3b3a17edb567436d5b0aad17a2748e02abfa3890fa493
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.18
|
