English(EN) OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

OptiPrime框架通过协议-硬件协同设计优化私有DNN推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了OptiPrime框架，旨在提高私有深度神经网络（DNN）推理的效率。该框架通过协同优化协议和硬件，解决了混合同态加密（HE）和多方计算（MPC）相关的延迟问题。OptiPrime引入了一种新的卷积HE协议，显著减少了传输的密文数量，从而缓解了网络通信瓶颈。此外，它还包含了一个轻量级的权重明文压缩系统和一个专门的数据流，以增强片上数据重用，从而带来显著的性能提升。 AI

影响这项研究可能有助于在隐私敏感的应用中更高效、更安全地部署AI模型。

排序理由该集群包含一篇详细介绍用于优化私有推理的新框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍用于优化私有推理的新框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jiangrui Yu, Ye Yu, Si Chen, Chenqi Lin, Wenxuan Zeng, Junfeng Fan, Mingyu Gao, Meng Li · 2026-09-16 04:00

OptiPrime：通过协议-硬件协同设计优化私有推理

arXiv:2609.16898v1 Announce Type: cross Abstract: Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the cost of significant latency overhead due to HE. Cu…

本文如何被排名

报道来源 [1]

OptiPrime：通过协议-硬件协同设计优化私有推理

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