Invasion of location privacy using online map services and smartphone sensors

Smartphone sensors potentially threaten the privacy of individuals, placing society at risk. Previous studies have demonstrated that smartphone sensors are susceptible to privacy intrusion. Inspired by this finding, we designed a mechanism of invasion that targets the location privacy of subway passengers. Specifically, we recovered the travel trajectories of subway passengers using sensor data and matched them with railway data collected from OpenStreetMap. This study primarily exploits an accelerometer and gyroscope, which are suitable for subway tracking because they operate appropriately in underground and indoor conditions. Although these sensors are easily influenced by passenger activity, we devised a method for recovering clean trajectories of subway passengers by utilizing gravitational acceleration and event detection methods. Subsequently, we conducted several experiments to prove the threat and feasibility of our proposals, even in the presence of human-generated noise (e.g., texting, watching videos, playing games, device rotation, and changing positions) influencing the sensor data. Specifically, we applied dynamic time warping (DTW) to obtain the costs between the reference data and reconstructed trace. Finally, a cost combination mechanism aggregated the DTW costs and predicted the best matches.