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Contact-tracing apps have potential benefits in helping health authorities toact swiftly to halt the spread of COVID-19. However, their effectiveness isheavily dependent on their installation rate, which may be influenced bypeople's perceptions of the utility of these apps and any potential privacyrisks due to the collection and releasing of sensitive user data (e.g., useridentity and location). In this paper, we present a survey study that examinedpeople's willingness to install six different contact-tracing apps afterinforming them of the risks and benefits of each design option (with aU.S.-only sample on Amazon Mechanical Turk, $N=208$). The six app designscovered two major design dimensions (centralized vs decentralized, basiccontact tracing vs. also providing hotspot information), grounded in ouranalysis of existing contact-tracing app proposals.  Contrary to assumptions of some prior work, we found that the majority ofpeople in our sample preferred to install apps that use a centralized serverfor contact tracing, as they are more willing to allow a centralized authorityto access the identity of app users rather than allowing tech-savvy users toinfer the identity of diagnosed users. We also found that the majority of oursample preferred to install apps that share diagnosed users' recent locationsin public places to show hotspots of infection. Our results suggest that appsusing a centralized architecture with strong security protection to do basiccontact tracing and providing users with other useful information such ashotspots of infection in public places may achieve a high adoption rate in theU.S.

Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps