Perceived Privacy Risks of Passive WiFi-Based Human Sensing Technologies: A Quantitative Study

Abstract

This study examined the relationship among passive WiFi sensing awareness, perceived sensing capability, perceived privacy risk, perceived surveillance exposure, trust in data governance, and privacy-protective behavioral intention within environments served by passive WiFi-based human sensing technologies in the United States. The rapid maturation of channel state information analytics, machine learning inference pipelines, and the emerging IEEE 802.11bf WLAN sensing standard has transformed ordinary wireless infrastructure into a pervasive human sensing medium capable of detecting presence, movement, activity, gesture, identity, and vital signs without any device carried by the sensed individual. The study used a quantitative, cross-sectional, correlational research design grounded in privacy calculus theory, contextual integrity theory, and protection motivation theory. Data were collected from 134 technology, networking, privacy, and facilities professionals working across residential, corporate, healthcare, retail, education, and public-sector environments in the United States using a structured Likert-scale survey instrument. Descriptive statistics, Pearson correlation analysis, and multiple linear regression analysis were conducted using SPSS, R, and Python statistical software to examine the relationships among the study variables. The findings revealed strong positive relationships among perceived sensing capability, perceived privacy risk, perceived surveillance exposure, and privacy-protective behavioral intention, alongside significant negative relationships involving trust in data governance. Perceived privacy risk demonstrated a high mean score of 4.12, while trust in data governance reported the lowest overall mean score of 2.87, indicating strong professional concern regarding the governance of passive sensing data. Pearson correlation analysis revealed a strong positive relationship between perceived privacy risk and privacy-protective behavioral intention (r = 0.74, p < 0.01), while perceived surveillance exposure also correlated strongly with protective intention (r = 0.69, p < 0.01). Multiple linear regression analysis indicated that the independent variables collectively explained 68% of the variance in privacy-protective behavioral intention (R² = 0.68, p < 0.001). Perceived privacy risk emerged as the strongest predictor of protective intention (β = 0.331, p < 0.001), followed by perceived surveillance exposure (β = 0.251, p < 0.001) and perceived sensing capability (β = 0.221, p = 0.002), while trust in data governance demonstrated a significant negative predictive relationship (β = −0.176, p = 0.004).