Smart cities promise safer streets, smoother traffic, and more efficient services, enabled by dense networks of urban sensors. Yet this infrastructure, often unnoticed by citizens, introduces pervasive privacy risks, from tracking, profiling, and sensitive inferences to subtle forms of self-censorship. Despite widespread deployment, little is known about how the public understands and perceives these sensing systems. To address this gap, we present an intervention based user study (n = 172) in which participants are exposed to data collection by six urban sensors, including cameras and alternative technologies commonly framed as privacy-preserving. Participants encounter either the sensors alone or sensors accompanied by real-time data visualizations. Our results reveal widespread misunderstanding of some sensors (radar, LiDAR, Wi-Fi, depth, and thermal imaging sensors), particularly their capacity for identification and for attribute inferences such as gender or age. We also identify persistent misconceptions, including the belief that Wi-Fi poses privacy risks only when users connect to public networks. While making sensors visible and visualizing collected data improves privacy awareness, these measures alone are not enough for citizens to understand the actual risks of urban sensing. We derive recommendations for privacy-respecting smart city environments grounded in citizens’ informational needs and expectations.

Brain Computer Interfaces (BCIs) are expanding beyond the medical realm into entertainment, wellness, and marketing. However, as consumer neurotechnology becomes more popular, privacy concerns arise due to the sensitive nature of brainwave data and its potential commodification. Attacks on privacy have been demonstrated and AI advancements in brain-to-speech and brain-to-image decoding pose a new unique set of risks. In this space, we contribute with the first user study (n=287) to understand people's neuroprivacy expectations and awareness of neurotechnology implications. Our analysis shows that, while users are interested in the technology, privacy is a critical issue for acceptability. The results underscore the importance of consent and the need for implementing effective transparency about neurodata sharing. Our insights provide a ground to analyse the gap in current privacy protection mechanisms, adding to the debate on how to design privacy-respecting neurotechnology.