The hidden life of signals: Time-domain inferences and other privacy attacks on everyday devices

In privacy research on radiofrequency-based protocols, the dominant focus has remained on Bluetooth, WiFi, and Zigbee, while a broader and arguably more consequential attack surface has gone largely unnoticed: the privacy risks created by the composition of everyday wireless protocols. Widely deployed systems such as KeeLoq remotes, vehicle TPMS sensors, and other sub-GHz devices continuously emit metadata and timing structure that, when analyzed jointly rather than in isolation, enable powerful behavioral inference. This work-in-progress paper argues that privacy leakage in these environments is not merely a property of individual protocols, but an emergent property of their interaction, correlation, and composition across devices, spaces, and routines. The resulting attack surface arises both from protocol metadata that directly degrades privacy and from the latent relationships between devices and the ways users move among and interact with them over time. We present preliminary evidence that these composed signals expose underappreciated opportunities for inference and tracking, and we outline a research agenda for characterizing and mitigating this broader class of privacy failures.