Efficiently Reducing Wi-Fi Sensing Privacy Risks Through Bandwidth-Aware Interference Injection

The integration of sensing capabilities into emerging wireless standards, such as 802.11 bf, presents an increasing threat to public privacy. Recent studies have demonstrated that even minor activities, such as finger movements on a keyboard, can be detected by exploiting Wi-Fi Channel State Information (CSI). To mitigate the privacy risks associated with Wi-Fi sensing, prior research has explored methods to disrupt the CSI measurement process by injecting interference into the wireless channel. However, current techniques often inundate the channel with excessive interference, resulting in a significant degradation of the wireless communication link. This paper proposes a spectrally efficient approach by investigating the sensitivity of Wi-Fi sensing to the CSI measurement rate, and designing interference that reduces sensing accuracy while preserving the integrity of the communication link. First, we quantify the accuracy of Wi-Fi-based keystroke recognition in relation to the CSI data rate. We then present theoretical justifications for the use of interference and demonstrate its impact on CSI data rates. Then, a method of adversarial interference is applied, reducing Wi-Fi sensing accuracy by 70 % in a keystroke detection scenario. Finally, a comprehensive trade-off study is conducted to demonstrate how interference can be optimized to protect privacy with savings of up to 21 % in bandwidth, ensuring minimal degradation of service quality.