1. Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use
- Author
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Stephen D. Soroka, Wil Duck, Brandi Dupervil, Dan Pollock, Patricia Sweeney, Brian Lee, J. Todd Weber, Jennifer Fuld, Jason Price, Benjamin Silk, and Lyndsay Bottichio
- Subjects
FOS: Computer and information sciences ,2019-20 coronavirus outbreak ,Information privacy ,Computer Science - Cryptography and Security ,Public Health Methodology ,Coronavirus disease 2019 (COVID-19) ,Internet privacy ,open data ,data paper ,Computer Science - Computers and Society ,03 medical and health sciences ,0302 clinical medicine ,Public use ,Data Anonymization ,Computers and Society (cs.CY) ,Humans ,030212 general & internal medicine ,Pandemics ,data privacy ,030505 public health ,SARS-CoV-2 ,business.industry ,Public Health, Environmental and Occupational Health ,De-identification ,COVID-19 ,Transparency (behavior) ,United States ,Open data ,de-identification ,Data quality ,Centers for Disease Control and Prevention, U.S ,0305 other medical science ,business ,Cryptography and Security (cs.CR) ,Confidentiality - Abstract
Objectives: Federal open data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial (STLT) partners. These initiatives advance understanding of health conditions and diseases by providing data to more researchers, scientists, and policymakers for analysis, collaboration, and valuable use outside CDC responders. This is particularly true for emerging conditions such as COVID-19 where we have much to learn and have evolving data needs. Since the beginning of the outbreak, CDC has collected person-level, de-identified data from jurisdictions and currently has over 8 million records, increasing each day. This paper describes how CDC designed and produces two de-identified public datasets from these collected data. Materials and Methods: Data elements were included based on the usefulness, public request, and privacy implications; specific field values were suppressed to reduce risk of reidentification and exposure of confidential information. Datasets were created and verified for privacy and confidentiality using data management platform analytic tools as well as R scripts. Results: Unrestricted data are available to the public through Data.CDC.gov and restricted data, with additional fields, are available with a data use agreement through a private repository on GitHub.com. Practice Implications: Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect privacy of de-identified individuals allow for improved data use. Automating data generation procedures allows greater and more timely sharing of data., Comment: 19 pages, 4 figures, 1 table, 5 supplements
- Published
- 2021