An investigation of the private-attribute leakage in WiFi sensing

Abstract

WiFi sensing is critical to many applications, such as localization, human activity recognition, and contact-less health monitoring. With metaverse and ubiquitous sensing advances, WiFi sensing becomes increasingly imperative. However, as shown in this paper, WiFi sensing data leaks users’ private attributes (e.g., height, weight, and gender), violating increasingly stricter privacy protection laws and regulations. To demonstrate the leakage of private attributes in WiFi sensing, we investigate two public WiFi sensing datasets and apply a deep learning model to recognize users’ private attributes. Our experimental results clearly show that our model can identify users’ private attributes in WiFi sensing data collected by general WiFi applications, with almost 100% accuracy for gender inference, less than 4 cm error for height inference, and about 4 kg error for weight inference, respectively. Our finding calls for research efforts to preserve data privacy while enabling WiFi sensing-based applications.