Guess Which Car Type I Am Driving: Information Leak via Driving Apps


Driving apps, such as navigation, fuel-price, and road services, have been deployed and used widely. The car-related nature of these services may motivate them to infer the type of their users’ vehicles. We first apply systematic analytics on real-world apps to show that the vehicle-type — seemingly unharmful — information may have serious privacy implications. Next, we demonstrate that attackers can harvest the features of these mobile apps to infer the car-type information in a stealthy way. Specifically, we explore the use of zero-permission mobile motion sensors to extract spectral features for differentiating the engines and body types of vehicles. Based on our experimental results of 17 different cars, we have achieved 82+% and 85+% overall accuracy in identifying three major engine types and four popular body types, respectively.

In ISOC Symposium on Vehicle Security and Privacy (VehicleSec ‘23)
Mert D. Pesé
Mert D. Pesé
Assistant Professor

My research interests include automotive security and privacy.