# spu

**Repository Path**: yoyo-deer/spu

## Basic Information

- **Project Name**: spu
- **Description**: SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
- **Primary Language**: C++
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://spu.readthedocs.io/
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 3
- **Created**: 2024-09-05
- **Last Updated**: 2024-09-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SPU: Secure Processing Unit

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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.

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).

If you would like to use SPU for research purposes, please check [research development guidelines](docs/SPU_gudience.pdf) from [@fionser](https://github.com/fionser).

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           | yes<sup>1</sup>| 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) and security advisories made by [VUL337@NISL@THU](https://netsec.ccert.edu.cn/vul337).