Leonie Reichert and Samuel Brack (Humboldt University of Berlin); Björn Scheuermann (Humboldt-University of Berlin)

The COVID-19 pandemic created various new challenges for our societies. Quickly discovering new infections using automated contact tracing without endangering privacy of the general public is one of these. Most discussions concerning architectures for contact tracing applications revolved around centralized against decentralized approaches. In contrast, the system proposed in this work builds on the idea of message based contact tracing to inform users about their risk. Our main contribution is the combination of a blind-signature approach to verify infections with an anonymous postbox service. In our evaluation, we analyze all components in our system for performance and privacy, as well as security. We also derive parameters for operating our system in a pandemic scenario.

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Jack P. K. Ma (The Chinese University of Hong Kong), Raymond K. H. Tai (The Chinese University of Hong Kong), Yongjun Zhao (Nanyang Technological University), Sherman S.M. Chow (The Chinese University of Hong Kong)

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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University), Dongyan Xu (Purdue University)

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Igor Linkov, PhD Senior Science and Technology Manager, US Army Engineer Research and Development Center; Senior Data Analyst (on detail), FEMA/HHS R1 COVID Task Force; Adjunct Professor, Carnegie Mellon University

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