A $35 Raspberry Pi and basic Python code can track a neighbor's movements through walls by sniffing their smartphone's wireless traffic.
Casual attackers can extract detailed daily routines of people nearby by analyzing the metadata of residential Wi-Fi signals. This technique does not require breaking encryption or hacking into devices to be effective. Privacy experts often argue that sophisticated surveillance is the exclusive domain of state actors or highly skilled hackers. This experiment proves that any neighbor with basic IT skills can reconstruct a household's private life from the sidewalk. Current wireless security protocols fail to protect the physical privacy of people in their own homes.
Noisy Networks, Nosy Neighbors: Simple Privacy Attacks Against Residential Wireless Traffic
arXiv · 2605.02553
Smart devices, such as light bulbs, TVs, fridges, etc., equipped with computing capabilities and wireless communication, are part of everyday life in many households. Previous work has already shown that a passive eavesdropper can derive private information, household routines, etc., from the network traffic of smart devices. However, existing attacks rely on capable adversaries with specialized machine learning expertise, labeled training data and reference devices, leaving it unclear how vulne