Conference Poster: 'Population grids protected by the Cell Key Method'

On 16th and 17th of April I visited the AnoSiDat conference (Anonymisierung für eine sichere Datennutzung). I had the opportunity to present preliminary results from my work done on 'Population grids protected by the Cell Key Method' in the poster session. The poster as well as corresponding R code to replicate its findings can be downloaded from my GitHub page.

Quoting the abstract:

"Population data for Census 2022 geographic grids (1ha) in Germany and several other nations will be protected by the Cell Key Method, a post-tabular perturbation technique. Is the fitness for purpose of the perturbed raster preserved? To address the question I run three archetypal spatial analysis tasks on a confidential (unprotected) raster and its perturbed counterparts (two variants) and compare the results."

Highlights

  • Illustrating an application of the ptable R package to create noise distributions for additive noise-based disclosure protection.
  • Comparing population counts for different land cover types before and after application of the protection method.
    Overlaying a population raster (top) and a land cover raster (bottom); here the population raster comes in two versions: unprotected raw data and data with noise added for confidentiality protection.

  • Simulation-based relative error of buffer-operations, if protected (noisy) data is used rather than unprotected original data.
    Aggregating population counts within buffer zones around a point of interest (such as a hospital or school) or line of interest (such as a road or railway line); the aggregate differs, if cell-level population counts come with added noise.

  • Quantifying divergence in hot spot identification from original vs. noisy data.

Additional information

The work was done as part of the AnigeD project, which is funded by the Federal Ministry of Education & Research and the European Union. It used data from the German 2011 population census and CORINE land cover by the Federal Office for Cartography. 

It is part of an ongoing effort to understand the effects of noisy confidentiality methods (such as are common for differentially private output mechanisms) on data products and the analyses based on them; see e.g. Kok et al. (2022), or Kenny et al. (2021).

Literature

C.T. Kenny, S. Kuriwaki, C. McCartan, E.T.R. Rosenman, T. Simko, K. Imai, "The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. census," Science Advances, vol.7, no.41, 2021.

M.R. Kok, M. Tuson, B. Turlach, B. Boruff, A. Vickery, D. Whyatt, "Impact of Australian Bureau of Statistics data perturbation techniques on the precision of census population counts, and the propagation of this impact in a geospatial analysis of high-risk foot hospital admissions among an indigenous population," Australian Geographer, vol.53, no.1, 2022, pp.105-126.

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