Mohit Kumar Jangid (Ohio State University) and Zhiqiang Lin (Ohio State University)

Being safer, cleaner, and more efficient, connected and autonomous vehicles (CAVs) are expected to be the dominant vehicles of future transportation systems. However, there are enormous security and privacy challenges while also considering the efficiency and and scalability. One key challenge is how to efficiently authenticate a vehicle in the ad-hoc CAV network and ensure its tamper-resistance, accountability, and non-repudiation. In this paper, we present the design and implementation of Vehicle-to-Vehicle (V2V) protocol by leveraging trusted execution environment (TEE), and show how this TEE-based protocol achieves the objective of authentication, privacy, accountability and revocation as well as the scalability and efficiency. We hope t hat our TEE-based V2V protocol can inspire further research into CAV security and privacy, particularly how to leverage TEE to solve some of the hard problems and make CAV closer to practice.

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HARPO: Learning to Subvert Online Behavioral Advertising

Jiang Zhang (University of Southern California), Konstantinos Psounis (University of Southern California), Muhammad Haroon (University of California, Davis), Zubair Shafiq (University of California, Davis)

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Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving

Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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Demo #6: Impact of Stealthy Attacks on Autonomous Robotic...

Pritam Dash, Mehdi Karimibiuki, and Karthik Pattabiraman (University of British Columbia)

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