Detecting mobility context over smartphones using typing and smartphone engagement patterns
Most of the latest context-based applications capture the mobility of a user using Inertial Measurement Unit (IMU) sensors like accelerometer and gyroscope which do not need explicit user-permission for application access. Although these sensors provide highly accurate mobility context information, existing studies have shown that they can lead to undesirable leakage of location information. To evade this breach of location privacy, many of the state-of-the-art studies suggest to impose stringent restrictions over the usage of IMU sensors. However, in this paper, we show that typing and smartphone engagement patterns can act as an alternative modality to sniff the mobility context of a user, even if the IMU sensors are not sampled at all. We develop an adversarial framework, named ConType, which exploits the signatures exposed by typing and smartphone engagement patterns to track the mobility of a user. Rigorous experiments with in-the-wild dataset show that ConType can track the mobility contexts with an average micro-F1 of 0.87 (±0.09), without using IMU data. Through additional experiments, we also show that ConType can track mobility stealthily with very low power and resource footprints, thus further aggravating the risk.
|IEEE International Conference on Pervasive Computing and Communications|
|Organisation||Distributed and Interactive Systems|
Chatterjee, S, Bhowmik, A, Singh, A, Ghosh, S, Mitra, B, & Chakraborty, S. (2020). Detecting mobility context over smartphones using typing and smartphone engagement patterns. In 2020 IEEE International Conference on Pervasive Computing and Communications. doi:10.1109/PerCom45495.2020.9127359