Tiny Sensors, Big Threats: Assessing Motion Sensor-based Fingerprinting in Mobile Systems

Motion sensors in mobile devices enable device fingerprinting through hardware-induced variations in sensed data. Although the feasibility of this identification technique has been demonstrated across numerous studies, the literature remains fragmented in terms of experimental setups and evaluation metrics—hindering a comprehensive understanding of its effectiveness and limitations. In this work, we provide the first systematization of the motion sensor fingerprinting landscape, structuring the pipeline into distinct stages and identifying key design parameters and countermeasures. Building on this, we develop a unified evaluation framework to assess each parameter in isolation under realistic conditions. Our results show that motion-based fingerprinting remains effective across diverse settings and classifier architectures, yet current countermeasures fail to provide reliable protection and often degrade data utility. We release our dataset to foster reproducibility and future work in this underexplored yet persistent privacy threat.