Privacy Protection in WiFi Sensing via CSI Fuzzing

The widespread adoption of WiFi has driven numerous WiFi-based wireless sensing applications. Researchers have utilized Channel State Information (CSI) from WiFi communications to develop various applications and systems, such as activity recognition, gesture recognition, and user authentication. However, unlike the payload data of packets, the CSI in WiFi packet headers lacks effective encryption mechanisms, posing a risk of privacy leakage. This paper proposes a privacy protection method for WiFi-based wireless sensing applications by controlling CSI through the modification of pilot symbols, specifically the Long Training Sequence (LTS), in WiFi packet headers. This approach affects the results of wireless sensing applications. To achieve encrypted protection of CSI, we designed a virtual channel model implemented at the FPGA level based on the OpenWiFi architecture. We simulate multipath effects to encrypt the IQ signal before its analog conversion. After passing through this virtual channel, the signal is transmitted through the real physical channel and collected by the receiver. Additionally, to further protect data privacy, we implemented a targeted protection algorithm for customized precise control of CSI in the current physical environment. Based on the current CSI data and the target CSI data, the algorithm customizes the parameter selection of the virtual channel model to generate a specific virtual channel for targeted encryption. Finally, our research verified the existence of privacy leakage issues in wireless sensing systems through experiments on respiration detection and human activity recognition based on CSI. We also validated the effectiveness of our designed virtual channel model in privacy protection.