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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Data Poisoning Attacks to Deep Learning Based Recommender Systems

Hai Huang (Tsinghua University), Jiaming Mu (Tsinghua University), Neil Zhenqiang Gong (Duke University), Qi Li (Tsinghua University), Bin Liu (West Virginia University), Mingwei Xu (Tsinghua University)

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PyPANDA: Taming the PANDAmonium of Whole System Dynamic Analysis

Luke Craig, Tim Leek (MIT Lincoln Laboratory), Andrew Fasano, Tiemoko Ballo (MIT Lincoln Laboratory, Northeastern University), Brendan Dolan-Gavitt (New York University), William Robertson (Northeastern University)

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HTTPS-Only: Upgrading all connections to https: in Web Browsers

Christoph Kerschbaumer, Julian Gaibler, Arthur Edelstein (Mozilla Corporation), Thyla van der Merwey (ETH Zurich)

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Demo #6: Impact of Stealthy Attacks on Autonomous Robotic...

Pritam Dash, Mehdi Karimibiuki, and Karthik Pattabiraman (University of British Columbia)

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