Understanding and Modeling of WiFi Signal-Based Indoor Privacy Protection

Existing WiFi recognition schemes are capable of discovering patterns of indoor semantics, such as human activity, identity, indoor environment, and so on. We note that channel state information (CSI) presents an opportunity for hackers to learn indoor privacy, however, currently there is a lack of...

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Vydáno v:IEEE internet of things journal Ročník 8; číslo 3; s. 2000 - 2010
Hlavní autoři: Zhang, Wei, Zhou, Siwang, Peng, Dan, Yang, Liang, Li, Fangmin, Yin, Hui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Activity recognition
channel state information (CSI)
deep network
Indoor environments
Internet of Things
Privacy
privacy protection
Security
Semantics
Support vector machines
Wireless fidelity
ISSN:2327-4662, 2327-4662
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Shrnutí:Existing WiFi recognition schemes are capable of discovering patterns of indoor semantics, such as human activity, identity, indoor environment, and so on. We note that channel state information (CSI) presents an opportunity for hackers to learn indoor privacy, however, currently there is a lack of security research on CSI. In this article, we are the first to discuss and define the security problem of CSI signals, which is further extended to the problems of nontargeted protection and targeted protection. To solve them, we present two types of adversarial autoencoder networks (AAENs). Through replacing the original signals with the generated adversarial ones, the protected semantic features are modified, and the significant features of the other semantics required to be recognized are reserved. Intensive evaluations demonstrate that with the proposed AAENs, the recognition accuracy of the protected semantic can be significantly decreased, while still maintaining the other semantics to be identified correctly.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.3015994