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.

View More Papers

Demo #7: Automated Tracking System For LiDAR Spoofing Attacks...

Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

Read More

KUBO: Precise and Scalable Detection of User-triggerable Undefined Behavior...

Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

Read More

Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

Read More

Denial-of-Service Attacks on C-V2X Networks

Natasa Trkulja, David Starobinski (Boston University), and Randall Berry (Northwestern University)

Read More