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EnglishThe proliferation of IoT-based services for smart cities, and especially those related to mobility, are ever becoming more relevant and gaining attention from a number of stake- holders. In our work, we tackle the problem of characterizing people movements in a urban environment by using WiFi sensors connected to the cellular network. In particular, we leverage WiFi probe requests transmitted by people’s smartphones and a ma- chine learning approach to detect people’s flows, while preserving users’ privacy. We validate our approach through a proof-of- concept testbed deployed in the proximity of our campus area. We consider two types of devices, namely, commercial, off-the- shelf WiFi scanners and ad-hoc designed scanners implemented with Raspberry PIs. They provide different levels of visibility of the captured traffic, preserving in different ways the privacy of the people’s movements. In our current work, we investigate the different trade-offs between mobility tracking accuracy and the level of provided people’s privacy.
A Privacy-preserving Scheme for Passive Monitoring of People’s Flows through WiFi Beacons / Gebru, Kalkidan. - ELETTRONICO. - (2022). ((Intervento presentato al convegno 2022 IEEE Consumer Communications & Networking Conference (CCNC) tenutosi a Las Vegas, NV, USA (Virtual conference due to COVID-19) nel 8–11 January 2022 [10.1109/CCNC49033.2022.9700591].
| Titolo: | A Privacy-preserving Scheme for Passive Monitoring of People’s Flows through WiFi Beacons | |
| Autori: | ||
| Data di pubblicazione: | 2022 | |
| Abstract: | The proliferation of IoT-based services for smart cities, and especially those related to ...mobility, are ever becoming more relevant and gaining attention from a number of stake- holders. In our work, we tackle the problem of characterizing people movements in a urban environment by using WiFi sensors connected to the cellular network. In particular, we leverage WiFi probe requests transmitted by people’s smartphones and a ma- chine learning approach to detect people’s flows, while preserving users’ privacy. We validate our approach through a proof-of- concept testbed deployed in the proximity of our campus area. We consider two types of devices, namely, commercial, off-the- shelf WiFi scanners and ad-hoc designed scanners implemented with Raspberry PIs. They provide different levels of visibility of the captured traffic, preserving in different ways the privacy of the people’s movements. In our current work, we investigate the different trade-offs between mobility tracking accuracy and the level of provided people’s privacy. | |
| ISBN: | 978-1-6654-3161-3 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2021254206.pdf | Articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| Gebru-Aprivacy.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2939773
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