Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App for increased understanding of its privacy-preserving design
This work addresses the problem of privacy concerns hindering widespread adoption of contact tracing apps among privacy-valuing populations, though it is incremental as it focuses on simulation and visualization rather than new methods.
The paper tackles the challenge of increasing trust in digital contact tracing apps by presenting a visual simulation of the Corona-Warn-App to demonstrate how it preserves user privacy and notifies users of infectious contacts to help contain COVID-19.
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.