# SPU: Secure Processing Unit
[](https://dl.circleci.com/status-badge/redirect/gh/secretflow/spu/tree/main)
[](https://pypi.org/project/spu/)
[](https://pypi.org/project/spu/)
[](https://securityscorecards.dev/viewer/?uri=github.com/secretflow/spu)
[](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 | 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).
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"description": "# SPU: Secure Processing Unit\n\n[](https://dl.circleci.com/status-badge/redirect/gh/secretflow/spu/tree/main)\n[](https://pypi.org/project/spu/)\n[](https://pypi.org/project/spu/)\n[](https://securityscorecards.dev/viewer/?uri=github.com/secretflow/spu)\n[](https://www.bestpractices.dev/projects/8311)\n\nSPU (Secure Processing Unit) aims to be a `provable`, `measurable` secure computation device,\nwhich provides computation ability while keeping your private data protected.\n\nSPU could be treated as a programmable device, it's not designed to be used directly.\nNormally we use SecretFlow framework, which use SPU as the underline secure computing device.\n\nCurrently, we mainly focus on `provable` security. It contains a secure runtime that evaluates\n[XLA](https://www.tensorflow.org/xla/operation_semantics)-like tensor operations,\nwhich use [MPC](https://en.wikipedia.org/wiki/Secure_multi-party_computation) as the underline\nevaluation engine to protect privacy information.\n\nSPU python package also contains a simple distributed module to demo SPU usage,\nbut it's **NOT designed for production** due to system security and performance concerns,\nplease **DO NOT** use it directly in production.\n\n## Contribution Guidelines\n\nIf you would like to contribute to SPU, please check [Contribution guidelines](CONTRIBUTING.md).\n\nThis documentation also contains instructions for [build and testing](CONTRIBUTING.md#build).\n\n## Installation Guidelines\n\n### Supported platforms\n\n| | Linux x86_64 | Linux aarch64 | macOS x64 | macOS Apple Silicon | Windows x64 | Windows WSL2 x64 |\n|------------|--------------|---------------|----------------|---------------------|----------------|---------------------|\n| CPU | yes | yes | yes<sup>1</sup>| yes | no | yes |\n| NVIDIA GPU | experimental | no | no | n/a | no | experimental |\n\n1. Due to CI resource limitation, macOS x64 prebuild binary is no longer available.\n\n### Instructions\n\nPlease follow [Installation Guidelines](INSTALLATION.md) to install SPU.\n\n### Hardware Requirements\n\n| General Features | FourQ based PSI | GPU |\n| ---------------- | --------------- | --- |\n| AVX/ARMv8 | AVX2/ARMv8 | CUDA 11.8+ |\n\n## Citing SPU\n\nIf you think SPU is helpful for your research or development, please consider citing our [paper](https://www.usenix.org/conference/atc23/presentation/ma):\n\n```text\n@inproceedings {spu,\n 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},\n title = {{SecretFlow-SPU}: A Performant and {User-Friendly} Framework for {Privacy-Preserving} Machine Learning},\n booktitle = {2023 USENIX Annual Technical Conference (USENIX ATC 23)},\n year = {2023},\n isbn = {978-1-939133-35-9},\n address = {Boston, MA},\n pages = {17--33},\n url = {https://www.usenix.org/conference/atc23/presentation/ma},\n publisher = {USENIX Association},\n month = jul,\n}\n```\n\n## Acknowledgement\n\nWe thank the significant contributions made by [Alibaba Gemini Lab](https://alibaba-gemini-lab.github.io).\n",
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