Aiping Xiong (Pennsylvania State University), Zekun Cai (Pennsylvania State University) and Tianhao Wang (University of Virginia)

Individuals’ interactions with connected autonomous vehicles (CAVs) involve sharing various data in a ubiquitous manner, raising novel challenges for privacy. The human factors of privacy must first be understood to promote consumers’ acceptance of CAVs. To inform the privacy research in the context of CAVs, we discuss how the emerging technologies development of CAV poses new privacy challenges for drivers and passengers. We argue that the privacy design of CAVs should adopt a user-centered approach, which integrates human factors into the development and deployment of privacy-enhancing technologies, such as differential privacy.

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Demo #10: Hijacking Connected Vehicle Alexa Skills

Wenbo Ding (University at Buffalo), Long Cheng (Clemson University), Xianghang Mi (University of Science and Technology of China), Ziming Zhao (University at Buffalo) and Hongxin Hu (University at Buffalo)

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Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

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Demo #5: Disclosing the Pringles Syndrome in Tesla FSD...

Zhisheng Hu (Baidu), Shengjian Guo (Baidu) and Kang Li (Baidu)

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