https://github.com/amir22010/pysyft

A library for encrypted, privacy preserving deep learning

https://github.com/amir22010/pysyft

Repository

A library for encrypted, privacy preserving deep learning

https://github.com/Amir22010/PySyft/blob/dev/

# 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 [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [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=)

## Pre-Installation

Optionally, we recommend that you install PySyft within the [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/overview.html) virtual environment. If you are using Windows, I suggest installing [Anaconda and using the Anaconda Prompt](https://docs.anaconda.com/anaconda/user-guide/getting-started/) to work from the command line.

```bash
conda create -n pysyft python=3
conda activate pysyft # some older version of conda require "source activate pysyft" instead.
conda install jupyter notebook
```

## Installation

> PySyft supports Python >= 3.6 and PyTorch 1.1.0

```bash
pip install syft
```

If you have an installation error regarding zstd, run this command and then re-try installing syft.

```bash
pip install --upgrade --force-reinstall zstd
```
If this still doesn't work, and you happen to be on OSX, make sure you have [OSX command line tools](https://railsapps.github.io/xcode-command-line-tools.html) installed and try again.

You can also install PySyft from source on a variety of operating systems by following this [installation guide](https://github.com/OpenMined/PySyft/blob/dev/INSTALLATION.md).

## Run Local Notebook Server

All the examples can be played with by running the command

```bash
make notebook
```

and selecting the pysyft kernel

## Use the Docker image

Instead of installing all the dependencies on your computer, you can run a notebook server (which comes with Pysyft installed) using [Docker](https://www.docker.com/). All you will have to do is start the container like this:

```bash
$ docker container run openmined/pysyft-notebook
```

You can use the provided link to access the jupyter notebook (the link is only accessible from your local machine).

> **_NOTE:_**
> If you are using Docker Desktop for Mac, the port needs to be forwarded to localhost. In that case run docker with:
> ```bash $ docker container run -p 8888:8888 openmined/pysyft-notebook ```
> to forward port 8888 from the container's interface to port 8888 on localhost and then access the notebook via http://127.0.0.1:8888/?token=... 


You can also set the directory from which the server will serve notebooks (default is /workspace).

```bash
$ docker container run -e WORKSPACE_DIR=/root openmined/pysyft-notebook
```

You could also build the image on your own and run it locally:

```bash
$ cd docker-image
$ docker image build -t pysyft-notebook .
$ docker container run pysyft-notebook
```

More information about how to use this image can be found [on docker hub](https://hub.docker.com/r/openmined/pysyft-notebook)

## 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.

## High-level Architecture

![alt text](art/PySyft-Arch.png "High-level Architecture")

## 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 5000+ 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!

## Troubleshooting

We have written an installation example in [this colab notebook](https://colab.research.google.com/drive/14tNU98OKPsP55Y3IgFtXPfd4frqbkrxK), 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!

[](https://udacity.com/) | [](https://github.com/coMindOrg/federated-averaging-tutorials) | [](http://ark.hn) | [](https://dropoutlabs.com/)
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## Disclaimer

Do NOT use this code to protect data (private or otherwise) - at present it is very insecure. Come back in a couple months.

## License

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

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