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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H M Sabbir Ahmad, Ehsan Sabouni, Akua Dickson (Boston University), Wei Xiao (Massachusetts Institute of Technology), Christos Cassandras, Wenchao Li (Boston University)

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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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“This is different from the Western world”: Understanding Password...

Aniqa Alam, Elizabeth Stobert, Robert Biddle (Carleton University)

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