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Cloud-based secure human action recognition with fully homomorphic encryption

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Abstract

The growth of cloud computing has encouraged resource-constrained data owners to upload skeleton data to the cloud for action identification, but this practice increases the risk of privacy breaches. Although the traditional anonymized privacy protection method protects the user privacy, it sacrifices the performance of action recognition. To solve the above problems, a Convolutional Neural Network (CNN) architecture compatible with Fully Homomorphic Encryption (FHE) is proposed in this paper, which can achieve secure action recognition without sacrificing the accuracy of action recognition. In addition, to solve the problem of low computational efficiency of the Residue Number System (RNS) variant of CKKS (RNS-CKKS) applied to CNN networks, a parallel fully homomorphic convolution method is designed to improve computational efficiency. To reduce the overhead of rotating key generation and transmission, a multi-layer key generation system is constructed. Finally, the superiority of the proposed model is verified on real data sets.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (62271096, U20A20157, U22A20102), Natural Science Foundation of Chongqing China (CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0034), University Innovation Research Group of Chongqing (CXQT20017), Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04), Scientific Research Foundation of CQUPT (2023071), and the Chongqing Postdoctoral Research Program Special Grant (2023CQBSHTB3092).

Funding

National Natural Science Foundation of China, 62271096, U20A20157, U22A20102, University Innovation Research Group of Chongqing, CXQT20017, Youth Innovation Group Support Program of ICE Discipline of CQUPT, SCIE-QN-2022-04, Scientific Research Foundation of CQUPT,2023071, Natural Science Foundation of Chongqing China, CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0034, the Chongqing Postdoctoral Research Program Special Grant, 2023CQBSHTB3092

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Author notes

  1. Ruyan Wang, Qinglin Zeng, Zhigang Yang, Puning Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Ruyan Wang, Qinglin Zeng & Puning Zhang

  2. School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Zhigang Yang

  3. The Advanced Network and Intelligent Connection Technology Key Laboratory, Chongqing, 400065, China

    Ruyan Wang, Qinglin Zeng, Zhigang Yang & Puning Zhang

  4. Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China

    Ruyan Wang, Qinglin Zeng, Zhigang Yang & Puning Zhang

Authors
  1. Ruyan Wang
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  2. Qinglin Zeng
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Contributions

RuyanWang:Funding acquisition, Project administration, Writing review & editing. Qinglin Zeng: Software, Writing- original draft, Visualization. Zhigang Yang: Conceptualization, Methodology, Supervision. Puning Zhang: Supervision, Writing review & editing.

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Correspondence to Zhigang Yang.

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Wang, R., Zeng, Q., Yang, Z. et al. Cloud-based secure human action recognition with fully homomorphic encryption. J Supercomput 81, 12 (2025). https://doi.org/10.1007/s11227-024-06512-z

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