COVID-19, digital privacy, and the social limits on data-focused public health responses
Open accessAbstract
The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a "privacy-first" approach which protects citizens' data at the cost of extremely limited access for public health authorities and researchers, and a "data-first" approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens' privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives - a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens' digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response.
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For many people in developing countries the digital divide is most apparent due to social and cultural barriers reinforced by poverty and a lack of education amongst the population.
“For many people in developing countries the digital divide is most apparent due to social and cultural barriers reinforced by poverty and a lack of education amongst the population (Barnes 2020;De et al. 2020;Fahey et al. 2020).”
From Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life (2020) · cites this paper as Fahey et al. 2020
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van Gend, J. E., Klooster, J. W. J. R. V. '., Bolman, C. A. W., & van Gemert‐Pijnen, J. E. W. C. (2022) · JMIR Formative Research · doi:10.2196/38904 · Cited by 4 · see citing works
Privacy and contact tracing efficacy
Benthall, S., Hatna, E., Epstein, J. M., & Strandburg, K. J. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0369 · Cited by 4 · see citing works
The Contributions of Customer Knowledge and Artificial Intelligence to Customer Satisfaction
Zada, I. (2022) · International Review of Management and Marketing · doi:10.32479/irmm.13314 · Cited by 4 · see citing works
- Authors
- Fahey, R. A., & Hino, A.
- Affiliations
- Waseda University (JP)
- Corresponding author
- Hino, Airo
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 55
- Pages
- 102181
- ISSN
- 0268-4012 (ISSN-L)
- PMC
- PMC7328565
- OpenAlex
- W3038461398
- Cited by
- 187
- References
- 9
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 22 · 2023: 32 · 2022: 50 · 2021: 55 · 2020: 16
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Information Management (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (20KK0026, 18H03642, 18K01423, 20H00066, 17H00971)
- Topics
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- COVID-19 pandemic, digital contact tracing, digital privacy, public health data, research data access, privacy compliance, pandemic response
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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