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Motion sensors (e.g., accelerometers) on smartphones have been demonstratedto be a powerful side channel for attackers to spy on users' inputs ontouchscreen. In this paper, we reveal another motion accelerometer-based attackwhich is particularly serious: when a person takes the metro, a maliciousapplication on her smartphone can easily use accelerator readings to trace her.We first propose a basic attack that can automatically extract metro-relateddata from a large amount of mixed accelerator readings, and then use anensemble interval classier built from supervised learning to infer the ridingintervals of the user. While this attack is very effective, the supervisedlearning part requires the attacker to collect labeled training data for eachstation interval, which is a significant amount of effort. To improve theefficiency of our attack, we further propose a semi-supervised learningapproach, which only requires the attacker to collect labeled data for a verysmall number of station intervals with obvious characteristics. We conduct realexperiments on a metro line in a major city. The results show that theinferring accuracy could reach 89\% and 92\% if the user takes the metro for 4and 6 stations, respectively.

We Can Track You If You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones