Digital contact tracing offers significant promise to help reduce the spread of SARS-CoV-2 and other viruses. Google and Apple joined together in 2020 to create the Google/Apple Exposure Notification (GAEN) framework to determine encounters with anonymous users later diagnosed COVID-19 positive. However, as GAEN lacks geospatial awareness, it is susceptible to geographically distributed replay attacks. Anonymous, low-cost, crowd-sourced replay attack networks deployed by malicious actors (or far away nation-state attackers) who utilize malicious (or innocent) users’ smartphones to capture and replay GAEN advertisements can drastically increase false-positive rates even in areas that otherwise exhibit low positivity rates. In response to this powerful replay attack, we introduce GAEN+ , a solution that enhances GAEN with geospatial awareness while maintaining user privacy, and demonstrate its ability to effectively prevent geographically distributed replay attacks.
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Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation
We present a novel, low cost framework for reconstructing surface contact movements during in-hand manipulations. Unlike many existing methods focused on hand pose tracking, ours models the behavior of contact patches, and by doing so is the first to obtain detailed contact tracking estimates for multi-contact manipulations. Our framework is highly accessible, requiring only low cost, readily available paint materials, a single RGBD camera, and a simple, deterministic interpolation algorithm. Despite its simplicity, we demonstrate the framework’s effectiveness over the course of several manipulations on three common household items. Finally, we demonstrate the use of a generated contact time series in manipulation learning for a simulated robot hand.
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- Award ID(s):
- 1925130
- NSF-PAR ID:
- 10293201
- Date Published:
- Journal Name:
- Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
- ISSN:
- 2153-0858
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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