Cloud-based secure human action recognition with fully homomorphic encryption

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.