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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Panel – Experiment Artifact Sharing: Challenges and Solutions

Moderator: Laura Tinnel (SRI International) Panelists: Clémentine Maurice (CNRS, IRIS); Martin Rosso (Eindhoven University of Technology); Eric Eide (U. Utah)

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MINOS: A Lightweight Real-Time Cryptojacking Detection System

Faraz Naseem (Florida International University), Ahmet Aris (Florida International University), Leonardo Babun (Florida International University), Ege Tekiner (Florida International University), A. Selcuk Uluagac (Florida International University)

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Awakening the Web's Sleeper Agents: Misusing Service Workers for...

Soroush Karami (University of Illinois at Chicago), Panagiotis Ilia (University of Illinois at Chicago), Jason Polakis (University of Illinois at Chicago)

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