P2ESA: Privacy-Preserving Environmental Sensor-Based Authentication
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| Xuất bản năm: | Sensors vol. 25, no. 15 (2025), p. 4842-4862 |
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| Tác giả chính: | |
| Tác giả khác: | , |
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MDPI AG
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| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s25154842 |2 doi | |
| 035 | |a 3239090971 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Andraž, Krašovec |u Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy; gianmarco.baldini@ec.europa.eu | |
| 245 | 1 | |a P<sup>2</sup>ESA: Privacy-Preserving Environmental Sensor-Based Authentication | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The presence of Internet of Things (IoT) devices in modern working and living environments is growing rapidly. The data collected in such environments enable us to model users’ behaviour and consequently identify and authenticate them. However, these data may contain information about the user’s current activity, emotional state, or other aspects that are not relevant for authentication. In this work, we employ adversarial deep learning techniques to remove privacy-revealing information from the data while keeping the authentication performance levels almost intact. Furthermore, we develop and apply various techniques to offload the computationally weak edge devices that are part of the machine learning pipeline at training and inference time. Our experiments, conducted on two multimodal IoT datasets, show that P2ESA can be efficiently deployed and trained, and with user identification rates of between 75.85% and 93.31% (c.f. 6.67% baseline), can represent a promising support solution for authentication, while simultaneously fully obfuscating sensitive information. | |
| 653 | |a Privacy | ||
| 653 | |a Biometrics | ||
| 653 | |a Computer terminals | ||
| 653 | |a Passwords | ||
| 653 | |a Access control | ||
| 653 | |a Internet of Things | ||
| 653 | |a Sensors | ||
| 700 | 1 | |a Baldini Gianmarco |u Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, Italy; gianmarco.baldini@ec.europa.eu | |
| 700 | 1 | |a Pejović Veljko |u Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia; veljko.pejovic@fri.uni-lj.si | |
| 773 | 0 | |t Sensors |g vol. 25, no. 15 (2025), p. 4842-4862 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239090971/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3239090971/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239090971/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |