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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.

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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

Deployment

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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