Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Raskar, Ramesh; Isabel, Schunemann,; Barbar, Rachel; Kristen, Vilcans,; Gray, Jim; Vepakomma, Praneeth; Kapa, Suraj; Nuzzo, Andrea; Gupta, Rajiv; Berke, Alex; Greenwood, Dazza; Keegan, Christian; Kanaparti, Shriank; Beaudry, Robson; Stansbury, David W.; Arcila, Beatriz Botero; Kanaparti, Rishank; Pamplona, Vitor F.; Benedetti, Francesco M.; Clough, A.; Das, Riddhiman; Jain, Kaushal; Louisy, Khahlil; Nadeau, Greg; Penrod, Steve; Rajaee, Yasaman; Singh, Rajesh; Storm, Greg; Werner, John S.; Chopra, Ayush; Gupta, Gauri; Sharma, V.

doi:10.48550/arxiv.2003.08567

Cited by 17 publications

(27 citation statements)

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“…Furthermore, it is focusing on the control of the pandemic as a whole. The protection of the individual turns out to reach a similar level as in the approach by Raskar et al [3].…”

Section: Tracing Contactssupporting

confidence: 53%

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Tracing Contacts to Control the COVID-19 Pandemic

Günther,

Günther

2020

Preprint

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The control of the COVID-19 pandemic requires a considerable reduction of contacts mostly achieved by imposing movement control up to the level of enforced quarantine. This has lead to a collapse of substantial parts of the economy. Carriers of the disease are infectious roughly 3 days after exposure to the virus. First symptoms occur later or not at all. As a consequence tracing the contacts of people identified as carriers is essential for controlling the pandemic. This tracing must work everywhere, in particular indoors, where people are closest to each other. Furthermore, it should respect people's privacy. The present paper presents a method to enable a thorough traceability with very little risk on privacy. In our opinion, the latter capabilities are necessary to control the pandemic during a future relaunch of our economy.

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“…Furthermore, it is focusing on the control of the pandemic as a whole. The protection of the individual turns out to reach a similar level as in the approach by Raskar et al [3].…”

Section: Tracing Contactssupporting

confidence: 53%

“…This observed complexity led us to the conclusion that automatic means of tracing are essential. Raskar et al [3] have analyzed an approach based on locating people with a particular focus on privacy-protection and self-protection against the disease. We follow a somewhat different approach.…”

Section: Tracing Contactsmentioning

confidence: 99%

Tracing Contacts to Control the COVID-19 Pandemic

Günther,

Günther

2020

Preprint

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“…Infected people's location histories are useful for contact-tracing, especially for tracing indirect contact (e.g., spread through shared surfaces or aerosol in a public spaces) which cannot be captured by proximity-based contact-tracing apps [20,51]. However, the use of location data in contact-tracing apps has been controversial and the Google/Apple exposure notification framework even forbids apps that built with it to collect location data [30,8] due to the risks of increased surveillance of all app users and privacy leak and stigmatization of infected users [72,73].…”

Section: Location Usementioning

confidence: 99%

“…Research has shown that users find knowing about infection hotspots useful and may be more willing to install an app that offers this feature [52,41]. To protect users' privacy, researchers have proposed technologies such as Safe Paths [51] that enable users to upload anonymized, redacted, and obfuscated location history.…”

Section: Location Usementioning

confidence: 99%

What Makes People Install a COVID-19 Contact-Tracing App? Understanding the Influence of App Design and Individual Difference on Contact-Tracing App Adoption Intention

Li¹,

Cobb²,

Jackie³

et al. 2020

Preprint

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Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates is a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment (N = 1963) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different subpopulations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one's perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one's perception of the app's security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.

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“…The former offers additional detail on the system architecture (i.e., centralized, decentralized, and hybrid), data management, and user concerns of existing solutions. Other notable reviews with similar discussion include Wen et al (2020); Raskar et al (2020); Cho et al (2020);Dar et al (2020); Lucivero et al (2020). Kuhn et al (2021) provides a formal framework for defining aspects of privacy in the context of proximity-based contact tracing.…”

Section: Related Workmentioning

confidence: 95%

ShareTrace: Contact Tracing with the Actor Model

Tatton¹,

Ayday²,

Yoo³

et al. 2022

Preprint

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Proximity-based contact tracing relies on user device interaction to estimate the spread of disease. ShareTrace is one such approach that has been shown to provide improved efficacy in tracking the spread of disease by also considering (in)direct forms of contact. In this work, we aim to provide an efficient and scalable formulation of ShareTrace by utilizing asynchronous, parallel, non-iterative message passing on a temporal graph. We also introduce a unique form of reachability, message reachability, that accounts for the dynamic nature of message passing and the temporal graph. Our evaluation on both synthetic and real-world temporal graphs indicates that correct parameter values optimize for accuracy and efficiency. In addition, we demonstrate that message reachability can accurately estimate the impact of the risk of a user on their contacts.

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Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Cited by 17 publications

References 0 publications

Tracing Contacts to Control the COVID-19 Pandemic

Tracing Contacts to Control the COVID-19 Pandemic

What Makes People Install a COVID-19 Contact-Tracing App? Understanding the Influence of App Design and Individual Difference on Contact-Tracing App Adoption Intention

ShareTrace: Contact Tracing with the Actor Model

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