Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps
Tianshi Li, Jackie (Junrui) Yang, Cori Faklaris, Jennifer King, Yuvraj, Agarwal, Laura Dabbish, Jason I. Hong

TL;DR
This study surveyed U.S. residents to understand their perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps, revealing preferences for centralized data collection and hotspot sharing, which could inform app design for higher adoption.
Contribution
It provides empirical insights into public preferences for contact-tracing app architectures and features, challenging assumptions favoring decentralized models.
Findings
Majority prefer centralized contact-tracing apps.
Most are willing to share location data for hotspots.
Centralized apps with security features may achieve higher adoption.
Abstract
Contact-tracing apps have potential benefits in helping health authorities to act swiftly to halt the spread of COVID-19. However, their effectiveness is heavily dependent on their installation rate, which may be influenced by people's perceptions of the utility of these apps and any potential privacy risks due to the collection and releasing of sensitive user data (e.g., user identity and location). In this paper, we present a survey study that examined people's willingness to install six different contact-tracing apps after informing them of the risks and benefits of each design option (with a U.S.-only sample on Amazon Mechanical Turk, ). The six app designs covered two major design dimensions (centralized vs decentralized, basic contact tracing vs. also providing hotspot information), grounded in our analysis of existing contact-tracing app proposals. Contrary to…
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Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Privacy, Security, and Data Protection · Privacy-Preserving Technologies in Data
