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Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data

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Privacy in Statistical Databases (PSD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13463))

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Abstract

Cell suppression is a widely used statistical disclosure control method for tabular data. Commonly, several linked tables are suppressed simultaneously. After publication, additional tables may be requested. In many contexts, new tables mean new ways of grouping and aggregating data that has already been published. The suppression of the new tables must be coordinated with the tables that have already been disseminated. A certain type of synthetic decimal numbers has proven to be very useful for this purpose. Based on the aggregation of these decimal numbers, one can decide whether a cell should be suppressed or not. An aggregation summing up to a whole number means the same as non-suppression. This article describes the theoretical basis for such decimal numbers. This is based on standard methodology from ordinary linear regression. The method is illustrated by a small example. In addition, two practical applications at Statistics Norway are presented, where one involves large hierarchical and linked tables where more than 50000 unique cells were primarily suppressed.

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Authors and Affiliations

  1. Statistics Norway, P.O. Box 8131 Dep., 0033, Oslo, Norway

    Øyvind Langsrud & Hege Marie Bøvelstad

Authors
  1. Øyvind Langsrud
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  2. Hege Marie Bøvelstad
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Correspondence to Øyvind Langsrud .

Editor information

Editors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. Télécom SudParis, Palaiseau, France

    Maryline Laurent

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Langsrud, Ø., Bøvelstad, H.M. (2022). Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data. In: Domingo-Ferrer, J., Laurent, M. (eds) Privacy in Statistical Databases. PSD 2022. Lecture Notes in Computer Science, vol 13463. Springer, Cham. https://doi.org/10.1007/978-3-031-13945-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-13945-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13944-4

  • Online ISBN: 978-3-031-13945-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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