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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MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

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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hbACSS: How to Robustly Share Many Secrets

Thomas Yurek (University of Illinois at Urbana-Champaign), Licheng Luo (University of Illinois at Urbana-Champaign), Jaiden Fairoze (University of California, Berkeley), Aniket Kate (Purdue University), Andrew Miller (University of Illinois at Urbana-Champaign)

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Fuzzing Configurations of Program Options

Zenong Zhang (University of Texas at Dallas), George Klees (University of Maryland), Eric Wang (Poolesville High School), Michael Hicks (University of Maryland), Shiyi Wei (University of Texas at Dallas)

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