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A Library for Private, Secure Deep Learning

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  • License: Apache-2.0
  • Author: Andrew Trask
  • Tags deep , learning , artificial , intelligence , privacy , secure , multi-party , computation
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# Introduction

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/OpenMined/PySyft/master) [![Build Status](https://travis-ci.org/OpenMined/PySyft.svg?branch=torch_1)](https://travis-ci.org/OpenMined/PySyft) [![Chat on Slack](https://img.shields.io/badge/chat-on%20slack-7A5979.svg)](https://openmined.slack.com/messages/team_pysyft) [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft.svg?type=small)](https://app.fossa.io/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft?ref=badge_small)

PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using [Multi-Party Computation (MPC)](https://en.wikipedia.org/wiki/Secure_multi-party_computation) within PyTorch. Join the movement on [Slack](http://slack.openmined.org/).

## PySyft in Detail

A more detailed explanation of PySyft can be found in the [paper on arxiv](https://arxiv.org/abs/1811.04017)

PySyft has also been explained in video form by [Siraj Raval](https://www.youtube.com/watch?v=39hNjnhY7cY&feature=youtu.be&a=)

## Installation

> PySyft supports Python >= 3.6 and PyTorch 1.0.0

`bash pip install syft ` ## Run Local Notebook Server All the examples can be played with by running the command `bash make notebook ` and selecting the pysyft kernel

## Try out the Tutorials

A comprehensive list of tutorials can be found [here](https://github.com/OpenMined/PySyft/tree/master/examples/tutorials)

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](https://github.com/OpenMined/PySyft/tree/master/CONTRIBUTING.md). 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](http://slack.openmined.org). The slack community is very friendly and great about quickly answering questions about the use and development of PySyft!

## Organizational Contributions

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

![drawing](https://raw.githubusercontent.com/coMindOrg/federated-averaging-tutorials/master/images/comindorg_logo.png)

[coMind Website](https://comind.org/) & [coMind Github](https://github.com/coMindOrg/federated-averaging-tutorials)

## Disclaimer

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

## License

[Apache License 2.0](https://github.com/OpenMined/PySyft/blob/master/LICENSE)

[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft?ref=badge_large)

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