spu 0.9.0b1

SPU aims to be a 'provable', 'measurable' secure computation device.

SPU: Secure Processing Unit

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 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 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:

@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.