Cryptographic inference for large language model via secret sharing

CHENG Ke; XIA Yuheng; DAI Chuanyun; FU Jiaxuan; ZHU Xinghui; SHEN Yulong

doi:10.11959/j.issn.1000-436x.2025115

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Cryptographic inference for large language model via secret sharing

Papers | 更新时间：2025-07-04

- Cryptographic inference for large language model via secret sharing
- Journal on Communications Vol. 46, Issue 6, Pages: 168-184(2025)
- 作者机构：
  
  1.西安电子科技大学计算机科学与技术学院，陕西西安 710126
  2.西安电子科技大学陕西省网络与系统安全重点实验室，陕西西安 710071
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2023YFB3107500);The National Natural Science Foundation of China(62402358;62220106004;92467201);The Natural Science Basic Research Program of Shaanxi Province(2025JC-YBQN-869);The Technology Innovation Leading Program of Shaanxi Province(2023KXJ-033);The Key Research and Development Program of Shandong Province of China(2023CXPT056);The Young Talent Fund of Association for Science and Technology in Shaanxi Province(20240138);The Open Topics from the Lion Rock Labs of Cyberspace Security(LRL24004);The Fundamental Research Funds for the Central Universities(ZDRC2202;KYFZ25005)
- DOI：10.11959/j.issn.1000-436x.2025115
  CLC： TP393
- Received：21 February 2025，
  
  Revised：2025-06-13，
  
  Published：25 June 2025
- 稿件说明：
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程珂,夏昱珩,代川云等.基于秘密分享的大语言模型密态推理[J].通信学报,2025,46(06):168-184.

CHENG Ke,XIA Yuheng,DAI Chuanyun,et al.Cryptographic inference for large language model via secret sharing[J].Journal on Communications,2025,46(06):168-184.
程珂,夏昱珩,代川云等.基于秘密分享的大语言模型密态推理[J].通信学报,2025,46(06):168-184. DOI： 10.11959/j.issn.1000-436x.2025115.

CHENG Ke,XIA Yuheng,DAI Chuanyun,et al.Cryptographic inference for large language model via secret sharing[J].Journal on Communications,2025,46(06):168-184. DOI： 10.11959/j.issn.1000-436x.2025115.

摘要

大语言模型推理服务可能导致用户输入提示信息泄露给服务器端或专有模型权重泄露给用户。安全多方计算、同态加密等密码学技术为解决上述问题提供了可行方案，但由于计算和通信开销过大，在处理大语言模型推理任务时难以实际应用。基于此，提出了基于轻量级秘密分享的大语言模型密态推理方案，在不泄露用户输入和模型权重的前提下，高效精准地实现大语言模型推理。实验表明，相较现有先进工作，所提方案密态推理效率提升1.2~10倍，通信开销减少20%~90%。

Abstract

Inference services based on large language models may lead to the leakage of user input hints to the server or proprietary model weights to the user. Cryptographic techniques such as secure multi-party computation and homomorphic encryption provide feasible solutions to the above problems

but they are still difficult to apply practically to the task of inference over large language models due to the excessive computational and communication overhead. Based on this

a lightweight secret-sharing-based cryptographic inference scheme for large language models was proposed

by which inference could be performed efficiently and accurately while ensuring that neither user inputs nor model parameters were revealed. The experimental results show that the proposed scheme improves the efficiency by 1.2~10 times and reduces the communication cost by 20%~90% compared with the existing state-of-the-art works.

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references

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