mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging. Xu, J., Bi, Z., Singha, A., Li, T., Chen, Y., & Zhang, Y. In 2023 32nd International Conference on Computer Communications and Networks (ICCCN), pages 1–10, July, 2023. ISSN: 2637-9430![mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.
@inproceedings{xu_mmlock_2023,
title = {{mmLock}: {User} {Leaving} {Detection} {Against} {Data} {Theft} via {High}-{Quality} {mmWave} {Radar} {Imaging}},
shorttitle = {{mmLock}},
url = {https://ieeexplore.ieee.org/abstract/document/10230151},
doi = {10.1109/ICCCN58024.2023.10230151},
abstract = {The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100\% true-positive rate in most scenarios.},
urldate = {2024-02-08},
booktitle = {2023 32nd {International} {Conference} on {Computer} {Communications} and {Networks} ({ICCCN})},
author = {Xu, Jiawei and Bi, Ziqian and Singha, Amit and Li, Tao and Chen, Yimin and Zhang, Yanchao},
month = jul,
year = {2023},
note = {ISSN: 2637-9430},
keywords = {Distance measurement, Point cloud compression, Radar, Radar detection, Radar imaging, Target recognition, Three-dimensional displays},
pages = {1--10},
}
Downloads: 1
{"_id":"gdvTp4XtsAoZHpets","bibbaseid":"xu-bi-singha-li-chen-zhang-mmlockuserleavingdetectionagainstdatatheftviahighqualitymmwaveradarimaging-2023","author_short":["Xu, J.","Bi, Z.","Singha, A.","Li, T.","Chen, Y.","Zhang, Y."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging","shorttitle":"mmLock","url":"https://ieeexplore.ieee.org/abstract/document/10230151","doi":"10.1109/ICCCN58024.2023.10230151","abstract":"The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.","urldate":"2024-02-08","booktitle":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","author":[{"propositions":[],"lastnames":["Xu"],"firstnames":["Jiawei"],"suffixes":[]},{"propositions":[],"lastnames":["Bi"],"firstnames":["Ziqian"],"suffixes":[]},{"propositions":[],"lastnames":["Singha"],"firstnames":["Amit"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Tao"],"suffixes":[]},{"propositions":[],"lastnames":["Chen"],"firstnames":["Yimin"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Yanchao"],"suffixes":[]}],"month":"July","year":"2023","note":"ISSN: 2637-9430","keywords":"Distance measurement, Point cloud compression, Radar, Radar detection, Radar imaging, Target recognition, Three-dimensional displays","pages":"1–10","bibtex":"@inproceedings{xu_mmlock_2023,\n\ttitle = {{mmLock}: {User} {Leaving} {Detection} {Against} {Data} {Theft} via {High}-{Quality} {mmWave} {Radar} {Imaging}},\n\tshorttitle = {{mmLock}},\n\turl = {https://ieeexplore.ieee.org/abstract/document/10230151},\n\tdoi = {10.1109/ICCCN58024.2023.10230151},\n\tabstract = {The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100\\% true-positive rate in most scenarios.},\n\turldate = {2024-02-08},\n\tbooktitle = {2023 32nd {International} {Conference} on {Computer} {Communications} and {Networks} ({ICCCN})},\n\tauthor = {Xu, Jiawei and Bi, Ziqian and Singha, Amit and Li, Tao and Chen, Yimin and Zhang, Yanchao},\n\tmonth = jul,\n\tyear = {2023},\n\tnote = {ISSN: 2637-9430},\n\tkeywords = {Distance measurement, Point cloud compression, Radar, Radar detection, Radar imaging, Target recognition, Three-dimensional displays},\n\tpages = {1--10},\n}\n\n","author_short":["Xu, J.","Bi, Z.","Singha, A.","Li, T.","Chen, Y.","Zhang, Y."],"key":"xu_mmlock_2023","id":"xu_mmlock_2023","bibbaseid":"xu-bi-singha-li-chen-zhang-mmlockuserleavingdetectionagainstdatatheftviahighqualitymmwaveradarimaging-2023","role":"author","urls":{"Paper":"https://ieeexplore.ieee.org/abstract/document/10230151"},"keyword":["Distance measurement","Point cloud compression","Radar","Radar detection","Radar imaging","Target recognition","Three-dimensional displays"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/8994433/collections/66SDI5S7/items?key=ozCmKMmrE26WwEN9PAdOLaiV&format=bibtex&limit=100","dataSources":["yM68t35RzDgJZajbn","Rxy8umoNP78qdq56v","nXz2cN22CbQDukEnJ"],"keywords":["distance measurement","point cloud compression","radar","radar detection","radar imaging","target recognition","three-dimensional displays"],"search_terms":["mmlock","user","leaving","detection","against","data","theft","via","high","quality","mmwave","radar","imaging","xu","bi","singha","li","chen","zhang"],"title":"mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging","year":2023,"downloads":1}