Passive, device-free localization of a person exploiting the Channel State Information (CSI) from Wi-Fi signals is quickly becoming a reality. While this capability would enable new applications and services, it also raises concerns about citizens’ privacy. In this work, we propose a carefully-crafted obfuscating technique against one of such CSI-based localization methods. In particular, we modify the transmitted I/Q samples by leveraging an irreversible randomized sequence. I/Q symbol manipulation at the transmitter distorts the location-specific information in the CSI while preserving communication, so that an attacker can no longer derive information on user's location. We test this technique against a Neural Network (NN)-based localization system and show that the randomization of the CSI makes undesired localization practically unfeasible. Both the localization system and the CSI randomization are implemented on real devices. The experimental results obtained in our laboratories show that the considered localization method works smoothly regardless of the environment, and that adding random information to the CSI prevents the localization, thus providing the community with a system that preserve location privacy and communication performance at the same time.
IEEE 802.11 CSI randomization to preserve location privacy: An empirical evaluation in different scenarios
Cominelli M.;Gringoli F.;Lo Cigno R.
;
2021-01-01
Abstract
Passive, device-free localization of a person exploiting the Channel State Information (CSI) from Wi-Fi signals is quickly becoming a reality. While this capability would enable new applications and services, it also raises concerns about citizens’ privacy. In this work, we propose a carefully-crafted obfuscating technique against one of such CSI-based localization methods. In particular, we modify the transmitted I/Q samples by leveraging an irreversible randomized sequence. I/Q symbol manipulation at the transmitter distorts the location-specific information in the CSI while preserving communication, so that an attacker can no longer derive information on user's location. We test this technique against a Neural Network (NN)-based localization system and show that the randomization of the CSI makes undesired localization practically unfeasible. Both the localization system and the CSI randomization are implemented on real devices. The experimental results obtained in our laboratories show that the considered localization method works smoothly regardless of the environment, and that adding random information to the CSI prevents the localization, thus providing the community with a system that preserve location privacy and communication performance at the same time.File | Dimensione | Formato | |
---|---|---|---|
ComNet-SI-WiNTEC-R1.pdf
solo utenti autorizzati
Tipologia:
Documento in Pre-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
3.73 MB
Formato
Adobe PDF
|
3.73 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.