PrivGait
An Energy Harvesting-based Privacy-Preserving User Identification System by Gait Analysis
Weitao Xu (City University of Hong Kong)
Wanli Xue (Cybersecurity Cooperative Research Centre)
Qi Lin (University of New South Wales)
Guohao Lan (TU Delft - Embedded Systems)
Xingyu Feng (Shenzhen University)
Bo Wei (University of Northumbria)
Chengwen Luo (Shenzhen University)
Wei Li (University of Sydney)
Albert Y. Zomaya (University of Sydney)
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Abstract
Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customized services. A fundamental requirement of these services is person identification. Although a variety of person-identification approaches has been proposed, they suffer from several limitations in practical applications, such as low energy efficiency, accuracy degradation, and privacy issue. This article proposes an energy-harvesting-based privacy-preserving gait recognition scheme for smart space, which is named PrivGait. In PrivGait, we extract discriminative features from 1-D gait signal and design an attention-based long short-term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of PrivGait, we design a proof-of-concept prototype using off-the-shelf energy-harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state of the art by 6%-10% and incurs low system cost while preserving user's privacy.