Cited by 3COVID-19 Digital Contact TracingPrivacy-Preserving Technologies in DataData-Driven Disease Surveillance

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Pandemic data challenges

Open access
·2020·Nature Machine Intelligence

Abstract

The worldwide outbreak of COVID-19 has led to great tragedy and poses unprecedented challenges for countries’ healthcare systems. Data has become an important instrument in the global fight against the unprecedented spread of the virus. But how will we ensure a return to previous forms of data privacy once the pandemic subsides?

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Crossref · 10.1038/s42256-020-0172-7

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  1. Mittelstadt, B., Benzler, J., Engelmann, L., Prainsack, B. & Vayena, E. Life Sci. Sci. Pol. 14, 9 (2018).

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Metadata

Venue
Nature Machine Intelligence
Published
2020-04-08
Volume
2
Issue
4
Pages
193-193
ISSN
2522-5839 (ISSN-L)
OpenAlex
W4235072991
Cited by
3
References
2
Citations by year
2020: 3
Type
journal article
Language
en
Open access
Locations
  • Nature Machine Intelligence (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Keywords
COVID-19 pandemic, data privacy, pandemic data, health data, data protection, privacy-preserving data sharing, post-pandemic recovery

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