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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WIP: On Robustness of Lane Detection Models to Physical-World...

Takami Sato (UC Irvine) and Qi Alfred Chen (UC Irvine)

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PoF: Proof-of-Following for Vehicle Platoons

Ziqi Xu (University of Arizona), Jingcheng Li (University of Arizona), Yanjun Pan (University of Arizona), Loukas Lazos (University of Arizona, Tucson), Ming Li (University of Arizona, Tucson), Nirnimesh Ghose (University of Nebraska–Lincoln)

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Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks

Chongzhou Fang (University of California, Davis), Han Wang (University of California, Davis), Najmeh Nazari (University of California, Davis), Behnam Omidi (George Mason University), Avesta Sasan (University of California, Davis), Khaled N. Khasawneh (George Mason University), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California, Davis)

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