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SPU: Secure Processing Unit

CircleCI Python PyPI version OpenSSF Scorecard

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-like tensor operations, which use MPC 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.

This documentation also contains instructions for build and testing.

Installation Guidelines

Supported platforms

Linux x86_64 Linux aarch64 macOS x86_64 macOS Apple Silicon Windows x86_64 Windows WSL2 x86_64
CPU yes experimental yes yes no yes
NVIDIA GPU experimental no no n/a no no

Instructions

Please follow Installation Guidelines to install SPU.

Hardware Requirements

General Features FourQ based PSI GPU
AVX/ARMv8 AVX2/ARMv8 CUDA 11.8+

Citing SPU

If you think SPU helpful for your research or development, please consider citing our paper:

@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.