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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datAFLow: Towards a Data-Flow-Guided Fuzzer

Adrian Herrera (Australian National University), Mathias Payer (EPFL), Antony Hosking (Australian National University)

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Demo #15: Remote Adversarial Attack on Automated Lane Centering

Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

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First, Fuzz the Mutants

Alex Groce (Northern Arizona Univerisity), Goutamkumar Kalburgi (Northern Arizona Univerisity), Claire Le Goues (Carnegie Mellon University), Kush Jain (Carnegie Mellon University), Rahul Gopinath (Saarland University)

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