IEEE 802.11 CSI randomization to preserve location privacy: An empirical evaluation in different scenarios

被引:20
作者
Cominelli, Marco [1 ]
Kosterhon, Felix [2 ]
Gringoli, Francesco [1 ]
Lo Cigno, Renato [1 ]
Asadi, Arash [3 ]
机构
[1] Univ Brescia, DII, Brescia, Italy
[2] Tech Univ Darmstadt, SEEMOO, Darmstadt, Germany
[3] Tech Univ Darmstadt, WISE SEEMOO, Darmstadt, Germany
关键词
Localization; Privacy; Channel state information; Neural networks; Wi-Fi; Randomization; Experiments and measures;
D O I
10.1016/j.comnet.2021.107970
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 21 条
[1]   Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints [J].
Abanto-Leon, Luis Fernando ;
Baeuml, Andreas ;
Sim, Gek Hong ;
Hollick, Matthias ;
Asadi, Arash .
PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS, 2020, 4 (03)
[2]   WiDeep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning [J].
Abbas, Moustafa ;
Elhamshary, Moustafa ;
Rizk, Hamada ;
Torki, Marwan ;
Youssef, Moustafa .
2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS (PERCOM), 2019,
[3]   See Through Walls with Wi-Fi! [J].
Adib, Fadel ;
Katabi, Dina .
ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 2013, 43 (04) :75-86
[4]  
Cai C., 2018, 2018 UB POS IND NAV
[5]  
Cominelli M., 2020, 14 ACM WORKSHOP WIRE, P1
[6]   Free Your CSI: A Channel State Information Extraction Platform For Modern Wi-Fi Chipsets [J].
Gringoli, Francesco ;
Schulz, Matthias ;
Link, Jakob ;
Hollick, Matthias .
WINTECH'19: PROCEEDINGS OF THE 13TH INTERNATIONAL WORKSHOP ON WIRELESS NETWORK TESTBEDS, EXPERIMENTAL EVALUATION & CHARACTERIZATION, 2019, :21-28
[7]   A Survey on Fusion-Based Indoor Positioning [J].
Guo, Xiansheng ;
Ansari, Nirwan ;
Hu, Fangzi ;
Shao, Yuan ;
Elikplim, Nkrow Raphael ;
Li, Lin .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (01) :566-594
[8]  
IEEE Standard for Information technology, 2016, IEEE Standard 802.1BA-2021 (Revision ofIEEE Std 802.1BA-2011
[9]  
Jiao Xianjun, 2020, IEEE VEHICULAR TECHN
[10]  
Kingma DP, 2014, ADV NEUR IN, V27