Exploiting and Securing WiFi for Pervasive Human Sensing

In recent years, the proliferation of WiFi-connected devices and related research has led to novel techniques of utilizing WiFi as sensors, i.e., inferring human activities through channel state information (CSI) perturbations. While this enables passive human sensing, it also introduces privacy risks from leaked WiFi signals that attackers can intercept, leading to threats like adversarial motion sensing. My research focuses on enhancing both the utility and security of WiFi sensing by addressing four key challenges: (1) lack of a secure sensing platform that also minimizes interference with normal WiFi communication, (2) environmental ambiguity caused by multipath effects, (3) confined sensing distance due to signal attenuation, and (4) limited scalability across different activities and users. I demonstrate solutions to these challenges through OneFi, a scalable WiFi sensing framework, and LeakyBeam, an adversarial WiFi sensing attack capable of long-range motion sensing. Finally, I discuss open problems and future challenges in achieving robust, scalable, and privacy-preserving WiFi sensing.