Jaroslav Kraus
Statistical Disclosure Control Methods for Harmonised Protection of Census Data
Číslo: 4/2021
Periodikum: Demografie
DOI: 10.54694/dem.0285
Klíčová slova: population and housing census, statistical disclosure control (SDC), grids
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Anotace:
The 2011 Population and Housing Census in the Czech Republic was accompanied by a significant change in the technology used to prepare course of the fieldwork, along with changes in how the data are processed and how the outputs are disseminated. Grids are regular polygon networks that divide the territory of country in a grid-like way/pattern into equally large territorial units, to which aggregate statistical data are assigned. The disadvantage of grids is that these are territorially small units that are often minimally populated. This mainly has implications for the protection of individual data, which is associated with statistical disclosure control (SDC).
The research question addressed in this paper is whether data protection (perturbation methods) leads to a change in the characteristics of the file either in terms of statistics of the whole file (i.e. for all grids) or in terms of spatial statistics, which indicate the spatial distribution of the analysed phenomenon. Two possible solutions to the issue of grid data protection are discussed. One comes from the Statistical Office of the European Communities (Eurostat) and the other from Cantabular, which is a product of the Sensible Code Company (SCC) based in Belfast.
According to the Cantabular methodology, one variant was processed, while according to the Eurostat methodology, two variants were calculated, which differ by the parameter settings for maximum noise D and the variance of noise V. The results of the descriptive statistics show a difference in absolute differences when Cantabular and Europstat solutions are compared. In the case of other statistics, the results are fully comparable. This paper is devoted to one specific type of census output. The question is to what extent these results are relevant for other types of census outputs. They differ fundamentally in the number of dimensions (grids have only two dimensions). It would therefore be appropriate to use SDC procedures that allow greater flexibility in defining SDC parameters.
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The research question addressed in this paper is whether data protection (perturbation methods) leads to a change in the characteristics of the file either in terms of statistics of the whole file (i.e. for all grids) or in terms of spatial statistics, which indicate the spatial distribution of the analysed phenomenon. Two possible solutions to the issue of grid data protection are discussed. One comes from the Statistical Office of the European Communities (Eurostat) and the other from Cantabular, which is a product of the Sensible Code Company (SCC) based in Belfast.
According to the Cantabular methodology, one variant was processed, while according to the Eurostat methodology, two variants were calculated, which differ by the parameter settings for maximum noise D and the variance of noise V. The results of the descriptive statistics show a difference in absolute differences when Cantabular and Europstat solutions are compared. In the case of other statistics, the results are fully comparable. This paper is devoted to one specific type of census output. The question is to what extent these results are relevant for other types of census outputs. They differ fundamentally in the number of dimensions (grids have only two dimensions). It would therefore be appropriate to use SDC procedures that allow greater flexibility in defining SDC parameters.