Feb. 6, 2024, 5:10 a.m. | Wuxuan Jiang Xiangjun Song Shenbai Hong Haijun Zhang Wenxin Liu Bo Zhao Wei Xu Yi Li

cs.CR updates on arXiv.org arxiv.org

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 …

accuracy adversarial attention challenges computation critical cs.cr cs.lg efficiency framework frameworks functions gpu linear machine machine learning mpc non novel party protocols secure computation spin

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