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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Demo #2: Sequential Attacks on Kalman Filter-Based Forward Collision...

Yuzhe Ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, and Jerry Zhu (University of Wisconsin–Madison)

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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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Demo #12: Too Afraid to Drive: Systematic Discovery of...

Ziwen Wan (UC Irvine), Junjie Shen (UC Irvine), Jalen Chuang (UC Irvine), Xin Xia (UCLA), Joshua Garcia (UC Irvine), Jiaqi Ma (UCLA) and Qi Alfred Chen (UC Irvine)

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On Utility and Privacy in Synthetic Genomic Data

Bristena Oprisanu (UCL), Georgi Ganev (UCL & Hazy), Emiliano De Cristofaro (UCL)

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