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SecretFlow is a unified framework for privacy preserving data intelligence and machine learning. To achieve this goal, it provides:

• An abstract device layer with plain devices and secret devices which encapsulate various cryptographic protocols.
• A device flow layer which modeling higher algorithms as device object flow and DAG.
• An algorithm layer to do data analysis and machine learning in horizontal or vertical data partition.
• A workflow layer that seamlessly integrates data processing, model training, 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>
>>> sf.reveal(data)
array([[0.98313141, 0.49663851, 0.47700297, 0.79132457],
       [0.16881197, 0.83516845, 0.09842819, 0.4015694 ],
       [0.33930415, 0.07568802, 0.88075431, 0.45873773]])

Contribution guide

For developers who want to contribute to SecretFlow, you can set up environment with the following instruction.

git clone https://github.com/secretflow/secretflow.git
conda create -n secretflow python=3.8
conda activate secretflow
pip install -r dev-requirements.txt -r requirements.txt