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Freedom of Information and Personal Confidentiality in Spatial COVID-19 Data

Author

Listed:
  • Beenstock Michael

    (Department of Economics, Hebrew University of Jerusalem, Mt Scopus, Jerusalem 91900Israel.)

  • Felsenstein Daniel

    (Department of Geography, Hebrew University of Jerusalem, Mt Scopus, Jerusalem 91900Israel.)

Abstract

We draw attention to how, in the name of protecting the confidentiality of personal data, national statistical agencies have limited public access to spatial data on COVID-19. We also draw attention to large disparities in the way that access has been limited. In doing so, we distinguish between absolute confidentiality in which the probability of detection is 1, relative confidentiality where this probability is less than 1, and collective confidentiality, which refers to the probability of detection of at least one person. In spatial data, the probability of personal detection is less than 1, and the probability of collective detection varies directly with this probability and COVID-19 morbidity. Statistical agencies have been concerned with relative and collective confidentiality, which they implement using the techniques of truncation, where spatial data are not made public for zones with small populations, and censoring, where exact data are not made public for zones where morbidity is small.

Suggested Citation

  • Beenstock Michael & Felsenstein Daniel, 2021. "Freedom of Information and Personal Confidentiality in Spatial COVID-19 Data," Journal of Official Statistics, Sciendo, vol. 37(4), pages 791-809, December.
    • Handle: RePEc:vrs:offsta:v:37:y:2021:i:4:p:791-809:n:11
    DOI: 10.2478/jos-2021-0035
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    References listed on IDEAS

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