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A library for encrypted, privacy preserving deep learning
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README.md

Introduction

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PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch. Join the movement on Slack.

PySyft in Detail

A more detailed explanation of PySyft can be found in the paper on arxiv

PySyft has also been explained in video form by Siraj Raval

Installation

PySyft supports Python >= 3.6 and PyTorch 1.0.0

pip install syft

Run Local Notebook Server

All the examples can be played with by running the command

make notebook

and selecting the pysyft kernel

Try out the Tutorials

A comprehensive list of tutorials can be found here

These tutorials cover how to perform techniques such as federated learning and differential privacy using PySyft.

Start Contributing

The guide for contributors can be found here. It covers all that you need to know to start contributing code to PySyft in an easy way.

Also join the rapidly growing community of 2500+ on Slack. The slack community is very friendly and great about quickly answering questions about the use and development of PySyft!

Troubleshooting

We have written an installation example in this colab notebook, you can use it as is to start working with PySyft on the colab cloud, or use this setup to fix your installation locally.

Organizational Contributions

We are very grateful for contributions to PySyft from the following organizations!

coMind

coMind Website & coMind Github

Disclaimer

Do NOT use this code to protect data (private or otherwise) - at present it is very insecure.

License

Apache License 2.0

FOSSA Status

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