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PrivGait: An Energy Harvesting-based Privacy-Preserving User Identification System by Gait Analysis

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Weitao Xu
  • Qi Lin
  • Wanli Xue
  • Guohao Lan
  • Xingyu Feng
  • Bo Wei
  • Chengwen Luo
  • Wei Li
  • Albert Zomaya
<mark>Journal publication date</mark>15/11/2022
<mark>Journal</mark>IEEE Internet of Things Journal
Issue number22
Number of pages13
Pages (from-to)22048-22060
Publication StatusPublished
Early online date15/06/21
<mark>Original language</mark>English


Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customised services. A fundamental requirement of these services is person identification. Although a variety of person identification approaches have been proposed, they suffer from several limitations in practical applications such as low energy efficiency, accuracy degradation, and privacy issue. This paper proposes an energy harvesting based privacy-preserving gait recognition scheme for smart space which is named . In , we extract discriminative features from one-dimensional 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 , 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&#x2013;10% and incurs low system cost while preserving user&#x2019;s privacy.