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Cloud-based secure human action recognition with fully homomorphic encryption

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Publicado en:The Journal of Supercomputing vol. 81, no. 1 (Jan 2025), p. 12
Autor principal: Wang, Ruyan
Otros Autores: Zeng, Qinglin, Yang, Zhigang, Zhang, Puning
Publicado:
Springer Nature B.V.
Materias:
Encryption
Accuracy
Datasets
Residue number systems
Multilayers
Communication
Artificial neural networks
Cloud computing
Optimization
Gender
Privacy
Computational efficiency
Identity
Methods
Algorithms
Utility computing
Human activity recognition
Energy consumption
Efficiency
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06512-z
Fuente:Advanced Technologies & Aerospace Database