Secretflow – A Unified Framework For Privacy-Preserving Data Analysis And Machine Learning
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.
Install
For users who want to try SecretFlow, you can install the current release from pypi. Note that it requires python version == 3.8, you can create a virtual environment with conda if not satisfied.
pip install -U secretflow
Try you first SecretFlow program
>>> import secretflow as sf
>>> sf.init(['alice', 'bob', 'carol'], num_cpus=8, log_to_driver=True)
>>> dev = sf.PYU('alice')
>>> import numpy as np
>>> data = dev(np.random.rand)(3, 4)
>>> data
<secretflow.device.device.pyu.PYUObject object at 0x7fdec24a15b0>
Getting started
- Getting started
- Tutorials help you to understand and use secretflow
- The api reference
Deployment
- Standalone or Cluster Mode
Contribution guide
For developers who want to contribute to SecretFlow, you can set up an environment with the following instruction.
git clone https://github.com/secretflow/secretflow.git
# optional
git lfs install
conda create -n secretflow python=3.8
conda activate secretflow
pip install -r dev-requirements.txt -r requirements.txt
Coding Style
We prefer black as our code formatter. For various editor users, please refer to editor integration. Pass -S, --skip-string-normalization
to black to avoid string quotes or prefixes normalization.
Disclaimer
Non-release versions of SecretFlow are prohibited to use in any production environment due to possible bugs, glitches, lack of functionality, security issues or other problems.
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