Yunpeng Luo (UC Irvine), Ningfei Wang (UC Irvine), Bo Yu (PerceptIn), Shaoshan Liu (PerceptIn) and Qi Alfred Chen (UC Irvine)

Autonomous Driving (AD) is a rapidly developing technology and its security issues have been studied by various recent research works. With the growing interest and investment in leveraging intelligent infrastructure support for practical AD, AD system may have new opportunities to defend against existing AD attacks. In this paper, we are the first t o systematically explore such a new AD security design space leveraging emerging infrastructure-side support, which we call Infrastructure-Aided Autonomous Driving Defense (I-A2D2). We first taxonomize existing AD attacks based on infrastructure-side capabilities, and then analyze potential I-A2D2 design opportunities and requirements. We further discuss the potential design challenges for these I-A2D2 design directions to be effective in practice.

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MIRROR: Model Inversion for Deep Learning Network with High...

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University), Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

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Effects of Knowledge and Experience on Privacy Decision-Making in...

Zekun Cai (Penn State University), Aiping Xiong (Penn State University)

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Demo #3: Detecting Illicit Drone Video Filming Using Cryptanalysis

Ben Nassi, Raz Ben-Netanel (Ben-Gurion University of the Negev), Adi Shamir (Weizmann Institute of Science), and Yuval Elovic (Ben-Gurion University of the Negev)

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Vehicle Lateral Motion Stability Under Wheel Lockup Attacks

Alireza Mohammadi (University of Michigan-Dearborn) and Hafiz Malik (University of Michigan-Dearborn)

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