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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Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial...

Wei Jia (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Zhaojun Lu (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Haichun Zhang (Huazhong University of Science and Technology), Zhenglin Liu (Huazhong University of Science and Technology), Jie Wang (Shenzhen Kaiyuan Internet Security Co., Ltd), Gang Qu (University…

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Tetrad: Actively Secure 4PC for Secure Training and Inference

Nishat Koti (IISc Bangalore), Arpita Patra (IISc Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (IISc, Bangalore)

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Hybrid Trust Multi-party Computation with Trusted Execution Environment

Pengfei Wu (School of Computing, National University of Singapore), Jianting Ning (College of Computer and Cyber Security, Fujian Normal University; Institute of Information Engineering, Chinese Academy of Sciences), Jiamin Shen (School of Computing, National University of Singapore), Hongbing Wang (School of Computing, National University of Singapore), Ee-Chien Chang (School of Computing, National University of Singapore)

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The Inconvenient Truths of Ground Truth for Binary Analysis

Jim Alves-Foss, Varsha Venugopal (University of Idaho)

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