Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - PrivGait
T2 - An Energy Harvesting-based Privacy-Preserving User Identification System by Gait Analysis
AU - Xu, Weitao
AU - Lin, Qi
AU - Xue, Wanli
AU - Lan, Guohao
AU - Feng, Xingyu
AU - Wei, Bo
AU - Luo, Chengwen
AU - Li, Wei
AU - Zomaya, Albert
PY - 2022/11/15
Y1 - 2022/11/15
N2 - 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 6–10% and incurs low system cost while preserving user’s privacy.
AB - 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 6–10% and incurs low system cost while preserving user’s privacy.
KW - Energy harvesting
KW - Feature extraction
KW - Gait recognition
KW - IoT security
KW - Privacy
KW - Sensors
KW - Smart spaces
KW - Wearable computers
KW - privacy preserving.
KW - smart space
UR - http://www.scopus.com/inward/record.url?scp=85112144596&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2021.3089618
DO - 10.1109/JIOT.2021.3089618
M3 - Journal article
VL - 9
SP - 22048
EP - 22060
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 22
ER -