Dongyao Chen (Shanghai Jiao Tong University), Mert D. Pesé (Clemson University), Kang G. Shin (University of Michigan, Ann Arbor)

ZOOX Best Paper Award Winner ($500 cash prize)!

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.

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Andrea Oliveri (EURECOM), Matteo Dell'Amico (University of Genoa), Davide Balzarotti (EURECOM)

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Hossein Fereidooni (Technical University of Darmstadt), Jan Koenig (University of Wuerzburg), Phillip Rieger (Technical University of Darmstadt), Marco Chilese (Technical University of Darmstadt), Bora Goekbakan (KOBIL, Germany), Moritz Finke (University of Wuerzburg), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Shuguo Zhuo, Nuo Li, Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

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Yuzhe Tang (Syracuse University), Kai Li (San Diego State University), and Yibo Wang and Jiaqi Chen (Syracuse University)

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