Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App for increased understanding of its privacy-preserving design

• URL: http://arxiv.org/abs/2202.02210v1
• Date: Fri, 4 Feb 2022 16:07:10 GMT
• Title: Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App for increased understanding of its privacy-preserving design
• Authors: Nikolas Gritsch, Benjamin Tegeler, Faheem Hassan Zunjani
• Abstract summary: The world is under an ongoing pandemic, COVID-19, of a scale last seen a century ago. Contact tracing is one of the most critical and highly effective tools for containing and breaking the chain of infections. Due to the invasive nature of contact tracing, it is very important to preserve the privacy of the users.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The world is under an ongoing pandemic, COVID-19, of a scale last seen a century ago. Contact tracing is one of the most critical and highly effective tools for containing and breaking the chain of infections especially in the case of infectious respiratory diseases like COVID-19. Thanks to the technological progress in our times, we now have digital mobile applications like the Corona-Warn-App for digital contact tracing. However, due to the invasive nature of contact tracing, it is very important to preserve the privacy of the users. Privacy preservation is important for increasing trust in the app and subsequently enabling its widespread usage in a privacy-valuing population. In this paper, we present a visual simulation of the working of the Corona-Warn-App to demonstrate how the privacy of its users is preserved, how they're notified of infectious contacts and how it helps in containing the spread of COVID-19.

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