Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11): 415-424.doi: 10.11896/jsjkx.241100101

• Information Security • Previous Articles     Next Articles

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

HE Yuyu1,2,3, ZHOU Feng1, TIAN Youliang3,4, XIONG Wei1, WANG Shuai1,2,3   

  1. 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).

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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

CLC Number: 

  • TP309.2
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