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 #9: Attacking Multi-Sensor Fusion based Localization in High-Level...

Junjie Shen, Jun Yeon Won, Zeyuan Chen and Qi Alfred Chen (UC Irvine)

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ditto: WAN Traffic Obfuscation at Line Rate

Roland Meier (ETH Zürich), Vincent Lenders (armasuisse), Laurent Vanbever (ETH Zürich)

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Hybrid Trust Multi-party Computation with Trusted Execution Environment

Pengfei Wu (School of Computing, National University of Singapore), Jianting Ning (College of Computer and Cyber Security, Fujian Normal University; Institute of Information Engineering, Chinese Academy of Sciences), Jiamin Shen (School of Computing, National University of Singapore), Hongbing Wang (School of Computing, National University of Singapore), Ee-Chien Chang (School of Computing, National University of Singapore)

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Time-Based CAN Intrusion Detection Benchmark

Deborah Blevins (University of Kentucky), Pablo Moriano, Robert Bridges, Miki Verma, Michael Iannacone, and Samuel Hollifield (Oak Ridge National Laboratory)

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