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)

Despite significant recent progress toward making multi-party computation (MPC) practical, no existing MPC library offers complete robustness---meaning guaranteed output delivery, including in the offline phase---in a network that even has intermittent delays. Importantly, several theoretical MPC constructions already ensure robustness in this setting. We observe that the key reason for this gap between theory and practice is the absence of efficient verifiable/complete secret sharing (VSS/CSS) constructions; existing CSS protocols either require a) challenging broadcast channels in practice or b) introducing computation and communication overhead that is at least quadratic in the number of players.

This work presents hbACSS, a suite of optimal-resilience asynchronous complete secret sharing protocols that are (quasi)linear in both computation and communication overhead. Towards developing hbACSS, we develop hbPolyCommit, an efficient polynomial commitment scheme that is (quasi)linear (in the polynomial degree) in terms of computation and communication overhead without requiring a trusted setup. We implement our hbACSS protocols, extensively analyze their practicality, and observe that our protocols scale well with an increasing number of parties. In particular, we use hbACSS to generate MPC input masks: a useful primitive which had previously only been calculated nonrobustly in practice.

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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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SpiralSpy: Exploring a Stealthy and Practical Covert Channel to...

Zhengxiong Li (University at Buffalo, SUNY), Baicheng Chen (University at Buffalo), Xingyu Chen (University at Buffalo), Huining Li (SUNY University at Buffalo), Chenhan Xu (University at Buffalo, SUNY), Feng Lin (Zhejiang University), Chris Xiaoxuan Lu (University of Edinburgh), Kui Ren (Zhejiang University), Wenyao Xu (SUNY Buffalo)

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Demo #4: Recovering Autonomous Robotic Vehicles from Physical Attacks

Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

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Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

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