MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices

Abstract

Various characteristics of mobile applications (apps) and associated in-app services can reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to restrict access to data related to mobile app usage. This paper outlines a novel approach to extracting detailed app usage information by analyzing electromagnetic (EM) signals emitted from mobile devices during app-related tasks. The proposed system, MagSpy, recovers user privacy information from magnetometer readings that do not require access permissions. This EM leakage becomes complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. To address these challenges, MagSpy employs multiple techniques to extract and identify signals related to app usage. Specifically, the geomagnetic offset signal is canceled using accelerometer and gyroscope sensor data, and a Cascade-LSTM algorithm is used to classify apps and in-app services. MagSpy also uses CWT-based peak detection and a Random Forest classifier to detect PIN inputs. A prototype system was evaluated on over 50 popular mobile apps with 30 devices. Extensive evaluation results demonstrate the efficacy of MagSpy in identifying in-app services (96% accuracy), apps (93.5% accuracy), and extracting PIN input information (96% top-3 accuracy).