P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Human Behavior Recognition

Haoda Wang, Chen Qiu, Chen Zhang, Jiantao Xu and Chunhua Su ()
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Haoda Wang: Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan
Chen Qiu: Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan
Chen Zhang: Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan
Jiantao Xu: Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan
Chunhua Su: Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan

Mathematics, 2024, vol. 12, issue 16, 1-16

Abstract: With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a risk of privacy leakage during interactions between the edge and the server. To tackle these problems, we propose an effective, privacy-preserving edge–cloud collaborative interaction scheme based on WiFi, named P-CA, for human behavior sensing. In our scheme, a convolutional autoencoder neural network is split into two parts. The shallow layers are deployed on the edge side for inference and privacy-preserving processing, while the deep layers are deployed on the server side to leverage its computing resources. Experimental results based on datasets collected from real testbeds demonstrate the effectiveness and considerable performance of the P-CA. The recognition accuracy can maintain 88%, although it could achieve about 94.8% without the mixing operation. In addition, the proposed P-CA achieves better recognition accuracy than two state-of-the-art methods, i.e., FedLoc and PPDFL, by 2.7% and 2.1%, respectively, while maintaining privacy.

Keywords: human behavior recognition; edge computing; privacy preservation; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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