Perform numpy-like analysis on data that remains in someone else's server

Quickstart

✅ Linux ✅ macOS* ✅ Windows†‡

PySyft = our numpy-like 🐍 Python library for computing on private data in someone else's Domain
PyGrid = our 🐳 docker / k8s / 🐧 vm Domain & Network Servers where private data lives
HAGrid = our handy 🛵 cli tool which makes deploying a Domain or Network server a one-liner

Read our 📚 Docs
Ask Questions ❔ in #support on Slack

Install Notes

HAGrid Requires: 🐍 python 🐙 git - Run: pip install hagrid
†Windows does not support ansible, preventing some remote deployment targets
PySyft Requires: 🐍 python 3.7+ - Run: pip install syft
*macOS Apple Silicon users need cmake: brew install cmake
‡Windows users must run this first: pip install jaxlib===0.3.7 -f https://whls.blob.core.windows.net/unstable/index.html
PyGrid Requires: 🐳 docker / k8s or 🐧 ubuntu VM - Run: hagrid launch ...

Versions

0.7.0 beta - dev branch 👈🏽
0.6.0 - Course 3
0.5.1 - Course 2 + M1 Hotfix
0.2.0 - 0.5.0 Deprecated

PySyft and PyGrid use the same version and its best to match them up where possible. We release weekly betas which can be used in each context: PySyft: pip install syft --pre PyGrid: hagrid launch ... tag=latest

HAGrid is a cli / deployment tool so the latest version of hagrid is usually the best.

What is Syft?

Syft is OpenMined's open source stack that provides secure and private Data Science in Python. Syft decouples private data from model training, using techniques like Federated Learning, Differential Privacy, and Encrypted Computation. This is done with a numpy-like interface and integration with Deep Learning frameworks, so that you as a Data Scientist can maintain your current workflow while using these new privacy-enhancing techniques.

Why should I use Syft?

Syft allows a Data Scientist to ask questions about a dataset and, within privacy limits set by the data owner, get answers to those questions, all without obtaining a copy of the data itself. We call this process Remote Data Science. It means in a wide variety of domains across society, the current risks of sharing information (copying data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the vast benefits such as innovation, insights and scientific discovery which secure access will provide.

No more cold calls to get access to a dataset. No more weeks of wait times to get a result on your query. It also means 1000x more data in every domain. PySyft opens the doors to a streamlined Data Scientist workflow, all with the individual's privacy at its heart.

Terminology

👨🏻‍💼 Data Owners	👩🏽‍🔬 Data Scientists
Provide `datasets` which they would like to make available for `study` by an `outside party` they may or may not `fully trust` has good intentions.	Are end `users` who desire to perform `computations` or `answer` a specific `question` using one or more data owners' `datasets`.
🏰 Domain Server	🔗 Network Server
Manages the `remote study` of the data by a `Data Scientist` and allows the `Data Owner` to manage the `data` and control the `privacy guarantees` of the subjects under study. It also acts as a `gatekeeper` for the `Data Scientist's` access to the data to compute and experiment with the results.	Provides services to a group of `Data Owners` and `Data Scientists`, such as dataset `search` and bulk `project approval` (legal / technical) to participate in a project. A network server acts as a bridge between it's members (`Domains`) and their subscribers (`Data Scientists`) and can provide access to a collection of `domains` at once.

Community

	_{^{🎥 PETs: Remote Data Science Unleashed - R gov 2021 🎥 Introduction to Remote Data Science - PyTorch 2021 🎥 The Future of AI Tools - PyTorch 2020 🎥 Privacy Preserving AI - MIT Deep Learning Series 🎥 Privacy-Preserving Data Science - TWiML Talk #241 🎥 Privacy Preserving AI - PyTorch Devcon 2019 📖 Towards general-purpose infrastructure for protect... 📖 Syft 0.5: A platform for universally deployable ... 📖 A generic framework for privacy preserving deep ...}}

Courses

Contributors

OpenMined and Syft appreciates all contributors, if you would like to fix a bug or suggest a new feature, please see our guidelines.

Supporters

Open Collective

OpenMined is a registered 501(c)(3) in the USA. We are funded by our gracious supporters on Open Collective.

Disclaimer

Syft is under active development and is not yet ready for pilots on private data without our assistance. As early access participants, please contact us via Slack or email if you would like to ask a question or have a use case that you would like to discuss.

License

Apache License 2.0
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ObliviousAI/PySyft