Hsun Lee (National Taiwan University), Yuming Hsu (National Taiwan University), Jing-Jie Wang (National Taiwan University), Hao Cheng Yang (National Taiwan University), Yu-Heng Chen (National Taiwan University), Yih-Chun Hu (University of Illinois at Urbana-Champaign), Hsu-Chun Hsiao (National Taiwan University)

Generating randomness by public participation allows participants to contribute randomness directly and verify the result's security. Ideally, the difficulty of participating in such activities should be as low as possible to reduce the computational burden of being a contributor. However, existing randomness generation protocols are unsuitable for this scenario because of scalability or usability issues. Hence, in this paper we present HeadStart, a participatory randomness protocol designed for public participation at scale. HeadStart allows contributors to verify the result on commodity devices efficiently, and provides a parameter $L$ that can make the result-publication latency $L$ times lower. Additionally, we propose two implementation improvements to speed up the verification further and reduce the proof size. The verification complexity of HeadStart is only $O(L times polylog(T) +log C)$ for a contribution phase lasting for time $T$ with $C$ contributions.

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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

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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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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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Shaik Sabiha (University at Buffalo), Keyan Guo (University at Buffalo), Foad Hajiaghajani (University at Buffalo), Chunming Qiao (University at Buffalo), Hongxin Hu (University at Buffalo) and Ziming Zhao (University at Buffalo)

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