VEH-Attack: Stealthy Tracking of Train Passengers With Side-Channel Attack on Vibration Energy Harvesting Wearables

Vibration energy harvesting (VEH) has emerged as a viable option for mobile devices that serves the dual purpose of generating power and sensing ambient vibrations. This paper highlights the location privacy leakage resulting from unrestricted access to seemingly innocuous VEH data on mobile devices. We present VEH-Attack, a side-channel attack that exploits an inference model and VEH data patterns generated from train vibrations, enabling precise tracking of train passengers. VEH-Attack achieves an accuracy of 97% and 83.13% for VEH derived data and actual VEH data, respectively, for trip length of 6 stations with the accuracy reaching 100% for longer trip lengths.