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 #1: Curricular Reinforcement Learning for Robust Policy in...

Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

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PMTUD is not Panacea: Revisiting IP Fragmentation Attacks against...

Xuewei Feng (Tsinghua University), Qi Li (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Baojun Liu (Tsinghua University), Xiaofeng Zheng (Institute for Network Sciences and Cyberspace, Tsinghua University; QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.), Qiushi Yang (QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.), Haixin Duan…

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EMS: History-Driven Mutation for Coverage-based Fuzzing

Chenyang Lyu (Zhejiang University), Shouling Ji (Zhejiang University), Xuhong Zhang (Zhejiang University & Zhejiang University NGICS Platform), Hong Liang (Zhejiang University), Binbin Zhao (Georgia Institute of Technology), Kangjie Lu (University of Minnesota), Raheem Beyah (Georgia Institute of Technology)

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