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OptiPrime framework optimizes private DNN inference with protocol-hardware co-design

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OptiPrime, a framework designed to improve the efficiency of private deep neural network (DNN) inference. This framework addresses the latency issues associated with hybrid homomorphic encryption (HE) and multi-party computation (MPC) by co-optimizing protocols and hardware. OptiPrime introduces a new HE protocol for convolutions that significantly reduces the number of transmitted ciphertexts, thereby mitigating network communication bottlenecks. Additionally, it incorporates a lightweight compression system for weight plaintexts and a specialized dataflow to enhance on-chip data reuse, leading to substantial performance gains. AI

IMPACT This research could lead to more efficient and secure deployment of AI models in privacy-sensitive applications.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology for optimizing private inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

OptiPrime framework optimizes private DNN inference with protocol-hardware co-design

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18 / 100
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The cluster contains a research paper detailing a new framework and methodology for optimizing private inference. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiangrui Yu, Ye Yu, Si Chen, Chenqi Lin, Wenxuan Zeng, Junfeng Fan, Mingyu Gao, Meng Li ·

    OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

    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…

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