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https://github.com/secretflow/spu

SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
https://github.com/secretflow/spu

privacy-preserving private-set-intersection secure-multiparty-computation

Last synced: 2 months ago
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SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.

Host: GitHub
URL: https://github.com/secretflow/spu
Owner: secretflow
License: apache-2.0
Created: 2022-07-02T09:05:04.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-04-08T04:21:27.000Z (2 months ago)
Last Synced: 2024-04-08T04:23:35.110Z (2 months ago)
Topics: privacy-preserving, private-set-intersection, secure-multiparty-computation
Language: C++
Homepage: https://www.secretflow.org.cn/docs/spu/en/
Size: 12.5 MB
Stars: 197
Watchers: 8
Forks: 85
Open Issues: 21
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md

Lists

awesome-jax - SPU - A domain-specific compiler and runtime suite to run JAX code with MPC(Secure Multi-Party Computation). <img src="https://img.shields.io/github/stars/secretflow/spu?style=social" align="center"> (Libraries / New Libraries)
awesome-mpc - SecretFlow-SPU - A domain-specific compiler and runtime suite, that aims to provide a secure computation service with provable security. SPU compiler uses XLA as its front-end Intermediate Representation (IR) and SPU runtime implements various MPC protocols. | [USENIX ATC'23](https://www.usenix.org/system/files/atc23-ma.pdf). (Software / Frameworks)
awesome-stars - secretflow/spu - SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected. (C++)
Awesome-SGX-Open-Source - https://github.com/secretflow/spu
mpc-awesome - SecretFlow-SPU - A domain-specific compiler and runtime suite, that aims to provide a secure computation service with provable security. SPU compiler uses XLA as its front-end Intermediate Representation (IR) and SPU runtime implements various MPC protocols. | [USENIX ATC'23](https://www.usenix.org/system/files/atc23-ma.pdf). (Software / Frameworks)

README

        # SPU: Secure Processing Unit

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/secretflow/spu/tree/main.svg?style=shield)](https://dl.circleci.com/status-badge/redirect/gh/secretflow/spu/tree/main)

[![Python](https://img.shields.io/pypi/pyversions/spu.svg)](https://pypi.org/project/spu/)

[![PyPI version](https://img.shields.io/pypi/v/spu)](https://pypi.org/project/spu/)

[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/secretflow/spu/badge)](https://securityscorecards.dev/viewer/?uri=github.com/secretflow/spu)

[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/8311/badge)](https://www.bestpractices.dev/projects/8311)

SPU (Secure Processing Unit) aims to be a `provable`, `measurable` secure computation device,

which provides computation ability while keeping your private data protected.

SPU could be treated as a programmable device, it's not designed to be used directly.

Normally we use SecretFlow framework, which use SPU as the underline secure computing device.

Currently, we mainly focus on `provable` security. It contains a secure runtime that evaluates

[XLA](https://www.tensorflow.org/xla/operation_semantics)-like tensor operations,

which use [MPC](https://en.wikipedia.org/wiki/Secure_multi-party_computation) as the underline

evaluation engine to protect privacy information.

SPU python package also contains a simple distributed module to demo SPU usage,

but it's **NOT designed for production** due to system security and performance concerns,

please **DO NOT** use it directly in production.

## Contribution Guidelines

If you would like to contribute to SPU, please check [Contribution guidelines](CONTRIBUTING.md).

This documentation also contains instructions for [build and testing](CONTRIBUTING.md#build).

## Installation Guidelines

### Supported platforms

|            | Linux x86_64 | Linux aarch64 | macOS x64      | macOS Apple Silicon | Windows x64    | Windows WSL2    x64 |

|------------|--------------|---------------|----------------|---------------------|----------------|---------------------|

| CPU        | yes          | yes           | yes¹| yes                 | no             | yes                 |

| NVIDIA GPU | experimental | no            | no             | n/a                 | no             | experimental        |

1. Due to CI resource limitation, macOS x64 prebuild binary is no longer available.

### Instructions

Please follow [Installation Guidelines](INSTALLATION.md) to install SPU.

### Hardware Requirements

| General Features | FourQ based PSI | GPU |

| ---------------- | --------------- | --- |

| AVX/ARMv8        | AVX2/ARMv8      | CUDA 11.8+ |

## Citing SPU

If you think SPU is helpful for your research or development, please consider citing our [paper](https://www.usenix.org/conference/atc23/presentation/ma):

```text

@inproceedings {spu,

    author = {Junming Ma and Yancheng Zheng and Jun Feng and Derun Zhao and Haoqi Wu and Wenjing Fang and Jin Tan and Chaofan Yu and Benyu Zhang and Lei Wang},

    title = {{SecretFlow-SPU}: A Performant and {User-Friendly} Framework for {Privacy-Preserving} Machine Learning},

    booktitle = {2023 USENIX Annual Technical Conference (USENIX ATC 23)},

    year = {2023},

    isbn = {978-1-939133-35-9},

    address = {Boston, MA},

    pages = {17--33},

    url = {https://www.usenix.org/conference/atc23/presentation/ma},

    publisher = {USENIX Association},

    month = jul,

}

```

## Acknowledgement

We thank the significant contributions made by [Alibaba Gemini Lab](https://alibaba-gemini-lab.github.io).