RoundRole: Unlocking the Efficiency of Multi-party Computation with Bandwidth-aware Execution
Xiaoyu Fan, Kun Chen, Jiping Yu, Xin Liu, Yunyi Chen, Wei Xu
摘要
—In privacy-preserving distributed computation systems like secure multi-party computation (MPC), cross-party communication is the primary bottleneck. Over the past two decades, numerous remarkable protocols have been proposed to reduce the overall communication complexity, substantially narrowing the gap between MPC and plaintext computations. However, these advances often overlook a crucial aspect: the asymmetric communication pattern. This imbalance results in significant bandwidth wastage, thereby “locking” the performance. In this paper, we propose RoundRole , a bandwidth-aware execution optimization for secret-sharing MPC. The key idea is to decouple the logical roles, which determine the communication patterns, from the physical nodes, which determine the band-width. By partitioning the overall protocol into parallel tasks and strategically mapping each logical role to a physical node for each task, RoundRole effectively allocates the communication workload in accordance with the inherent protocol communication volume and the physical bandwidth. This execution-level optimization fully leverages network resources and “unlocks” the efficiency. We integrate RoundRole on top of ABY3, one of the widely used open-source MPC frameworks. Extensive evaluations across nine protocols under six diverse network settings (with homogeneous and heterogeneous bandwidths) demonstrate significant performance improvements, achieving up to 7.1 × speedups.
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它引用的顶会 Paper23
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 被引用 898 次
- ABY2.0: Improved Mixed-Protocol Secure Two-Party ComputationArpita Patra, Thomas Schneider, Ajith Suresh, Hossein YalameUSENIX Security 2021 · 被引用 307 次
- CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUSijun Tan, Brian Knott, Yuan Tian, David J. WuS&P 2021 · 被引用 241 次
- Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious SecurityAnders P. K. Dalskov, Daniel Escudero, Marcel KellerUSENIX Security 2021 · 被引用 174 次
- BOLT: Privacy-Preserving, Accurate and Efficient Inference for TransformersQi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng 等S&P 2024 · 被引用 149 次
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