基于加性秘密共享的轻量级隐私保护移动传感分类框架

doi:10.11896/jsjkx.241100101

计算机科学 ›› 2025, Vol. 52 ›› Issue (11): 415-424.doi: 10.11896/jsjkx.241100101

基于加性秘密共享的轻量级隐私保护移动传感分类框架

何宇宇^1,2,3, 周凤¹, 田有亮^3,4, 熊伟¹, 王帅^1,2,3

1 贵州大学计算机科学与技术学院公共大数据国家重点实验室贵阳 550025
2 贵州省密码学与区块链技术特色重点实验室贵阳 550025
3 贵州大学密码学与数据安全研究所贵阳 550025
4 贵州大学大数据与信息工程学院贵阳 550025

收稿日期:2024-11-18 修回日期:2025-02-21 出版日期:2025-11-15 发布日期:2025-11-06
通讯作者: 周凤(41544782@qq.com)
作者简介:(heyuyu97@163.com)
基金资助:
国家重点研发计划(2021YFB3101100);国家自然科学基金(62272123);贵州省高层次创新型人才项目(黔科合平台人才[2020]6008);贵州省科技计划项目(黔科合平台人才[2020]5017,黔科合支撑[2022]一般065,黔科合战略找矿[2022]ZD001);贵阳市科技计划项目(筑科合[2022]2-4)

Lightweight Privacy-preserving Mobile Sensing Classification Framework Based on AddictiveSecret Sharing

HE Yuyu^1,2,3, ZHOU Feng¹, TIAN Youliang^3,4, XIONG Wei¹, WANG Shuai^1,2,3

1 State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
2 Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology,Guiyang 550025,China
3 Institute of Cryptography & Data Security,Guizhou University,Guiyang 550025,China
4 College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China

Received:2024-11-18 Revised:2025-02-21 Online:2025-11-15 Published:2025-11-06
About author:HE Yuyu,born in 2000,postgraduate.His main research interests include privacy-preserving machine learning and secure multi-party computation.
ZHOU Feng,born in1976,postgra-duate,associate professor,postgraduate supervisor.Her main research interests include big data security and privacy protection.
Supported by:
National Key R&D Program of China(2021YFB3101100),National Natural Science Foundation of China(62272123),Project of High-level Innovative Talents of Guizhou Province([2020]6008),Science and Technology Program of Guizhou Province([2020]5017,[2022]065,[2022]ZD001) and Science and Technology Program of Guiyang([2022]2-4).

摘要/Abstract

摘要： 针对在移动传感设备上部署卷积神经网络模型出现的数据隐私泄露问题,以及隐私保护目标分类框架中服务器交互计算导致通信开销过高的挑战,提出了一种基于加性秘密共享的轻量级隐私保护移动传感目标分类框架(LPMS)。该框架确保移动传感设备在交换数据时不会泄露隐私信息,同时显著降低通信开销和计算开销。首先,利用加性秘密共享技术构建了一系列不依赖计算密集型密码原语的安全计算协议,以实现安全高效的神经网络计算;其次,构建了一种三维混沌加密方案,防止原始数据在上传至边缘服务器的过程中被攻击者窃取;最后,通过理论分析与安全性证明,验证了LPMS框架的正确性及安全性。实验结果表明,与PPFE方案相比,LPMS方案将模型计算开销降低了73.33%,通信开销减少了68.36%。

关键词: 移动传感设备, 卷积神经网络, 隐私保护, 加性秘密共享, 混沌加密

Abstract: To address data privacy leakage in deploying convolutional neural network models on mobile sensing devices,as well as the challenge of excessive communication overhead caused by server interaction computations in privacy-preserving target classification frameworks,a lightweight privacy-preserving mobile sensing object classification framework(LPMS) based on additive secret sharing is proposed.This framework ensures that mobile sensing devices maintain data confidentiality during data exchanges while significantly reducing both communication and computational overhead.Firstly,a series of secure computing protocols are developed using additive secret sharing technology,avoiding reliance on computationally intensive cryptographic primitives to facilitate efficient and secure neural network computations.Secondly,a three-dimensional chaotic encryption scheme is introduced to protect original data from potential attackers during uploads to the edge server.Finally,the correctness and security of the LPMS framework are validated through theoretical analysis and security proofs.Experimental results demonstrate that,compared to the PPFE scheme,the LPMS framework reduces model computation overhead by 73.33% and communication overhead by 68.36%.

Key words: Mobile sensing devices, Convolutional neural networks, Privacy-preserving, Additive secret sharing, Chaotic encryption

中图分类号:

TP309.2

何宇宇, 周凤, 田有亮, 熊伟, 王帅. 基于加性秘密共享的轻量级隐私保护移动传感分类框架[J]. 计算机科学, 2025, 52(11): 415-424. https://doi.org/10.11896/jsjkx.241100101

HE Yuyu, ZHOU Feng, TIAN Youliang, XIONG Wei, WANG Shuai. Lightweight Privacy-preserving Mobile Sensing Classification Framework Based on AddictiveSecret Sharing[J]. Computer Science, 2025, 52(11): 415-424. https://doi.org/10.11896/jsjkx.241100101

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基于加性秘密共享的轻量级隐私保护移动传感分类框架

Lightweight Privacy-preserving Mobile Sensing Classification Framework Based on AddictiveSecret Sharing

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