{"title":"Passive User Profiling Using Array of Sustainable Backscatter Tags","authors":"Haoming Wang;Hao Lai;Amus Chee Yuen Goay;Deepak Mishra;Aruna Seneviratne;Eliathamby Ambikairajah","doi":"10.1109/LCOMM.2025.3576750","DOIUrl":null,"url":null,"abstract":"Wireless technology is increasingly used for real-time identity verification through active sensors, but traditional biometrics like fingerprint scanning raise privacy concerns due to their invasive nature. To address this, we propose a first-ever Backscatter Communication (BackCom)-based user profiling and commodity RFID height or weight profiling demonstration, using backscatter signals from RFID tags placed in the environment rather than on individuals. These battery-free, energy-harvesting tags offer a sustainable, privacy-preserving method for passive identity recognition. By applying a pre-trained linear machine learning algorithm to the Received Signal Strength Indicator (RSSI) data from RFID tags, we can identify individuals based on the modulation of the backscatter signal caused by their unique physical characteristics. Our system achieves up to 90.2% accuracy in identifying individuals from a set of seven. Additionally, we employ an unsupervised anomaly detection method that combines ResNet-18 feature extraction with Principal Component Analysis (PCA), yielding over 90% overall accuracy in distinguishing between known and unknown subjects.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 8","pages":"1824-1828"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11026024/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Wireless technology is increasingly used for real-time identity verification through active sensors, but traditional biometrics like fingerprint scanning raise privacy concerns due to their invasive nature. To address this, we propose a first-ever Backscatter Communication (BackCom)-based user profiling and commodity RFID height or weight profiling demonstration, using backscatter signals from RFID tags placed in the environment rather than on individuals. These battery-free, energy-harvesting tags offer a sustainable, privacy-preserving method for passive identity recognition. By applying a pre-trained linear machine learning algorithm to the Received Signal Strength Indicator (RSSI) data from RFID tags, we can identify individuals based on the modulation of the backscatter signal caused by their unique physical characteristics. Our system achieves up to 90.2% accuracy in identifying individuals from a set of seven. Additionally, we employ an unsupervised anomaly detection method that combines ResNet-18 feature extraction with Principal Component Analysis (PCA), yielding over 90% overall accuracy in distinguishing between known and unknown subjects.
期刊介绍:
The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.