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PhyCloak: Obfuscating Sensing from Communication Signals
Yue Qiao, Ouyang Zhang, Wenjie Zhou, Kannan Srinivasan, and Anish Arora, The Ohio State University
Awarded Best Student Paper
Recognition of human activities and gestures using preexisting WiFi signals has been shown to be feasible in recent studies. Given the pervasiveness of WiFi signals, this emerging sort of sensing poses a serious privacy threat. This paper is the first to counter the threat of unwanted or even malicious communication based sensing: it proposes a blackbox sensor obfuscation technique PhyCloak which distorts only the physical information in the communication signal that leaks privacy. The data in the communication signal is preserved and, in fact, the throughput of the link is increased with careful design. Moreover, the design allows coupling of the PhyCloak module with legitimate sensors, so that their sensing is preserved, while that of illegitimate sensors is obfuscated. The effectiveness of the design is validated via a prototype implementation on an SDR platform
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