A Library for Private, Secure Deep Learning
Project description
Introduction
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 Website & coMind Github
Disclaimer
Do NOT use this code to protect data (private or otherwise) - at present it is very insecure.
License
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