Spin: An Efficient Secure Computation Framework with GPU Acceleration
<|reference_start|>Spin: An Efficient Secure Computation Framework with GPU Acceleration: Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks. Spin is a GPU-accelerated MPC framework that supports multiple computation parties and a dishonest majority adversarial setup. We propose optimized protocols for non-linear functions that are critical for machine learning, as well as several novel optimizations specific to attention that is the fundamental unit of Transformer models, allowing Spin to perform non-trivial CNNs training and Transformer inference without sacrificing security. At the backend level, Spin leverages GPU, CPU, and RDMA-enabled smart network cards for acceleration. Comprehensive evaluations demonstrate that Spin can be up to $2\times$ faster than the state-of-the-art for deep neural network training. For inference on a Transformer model with 18.9 million parameters, our attention-specific optimizations enable Spin to achieve better efficiency, less communication, and better accuracy.<|reference_end|>
@article{jiang2024spin:,
    title={Spin: An Efficient Secure Computation Framework with GPU Acceleration},
    author={Wuxuan Jiang, Xiangjun Song, Shenbai Hong, Haijun Zhang, Wenxin Liu,
  Bo Zhao, Wei Xu, Yi Li},
    journal={arXiv preprint arXiv:2402.02320},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02320},
    primaryClass={cs.CR cs.LG}
}