Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19597, first published .
Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes… https://t.co/p70ou0qnBR

2:27 PM · Nov 26, 2020

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Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes… https://t.co/TfPCpUAJtP

5:55 PM · Nov 26, 2020

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Korea University Anam Hospital uses ARX to anonymize real-world health data stored in the Observational Medical Out… https://t.co/3pJYA0Url5

2:45 PM · Nov 28, 2020

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https://t.co/mhVhEyxMkG This study proposes and evaluates a "de-identification strategy" that is comprised of seve… https://t.co/yeD97H1QIe

9:13 PM · Jun 26, 2022

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