Privacy Protected maps

sdcSpatial contains functions to create spatial distribution maps, assess the risk of disclosure on a location and to suppress or adjust revealing values at certain locations.

Details

sdcSpatial working horse is the sdc_raster() object upon which the following methods can be applied:

Sensitivity assessment

• plot.sdc_raster(), plot_sensitive()

• print

• is_sensitive()

Protection methods

• remove_sensitive()

• protect_smooth()

• protect_quadtree()

Extraction

• sum, extract the sum layer from a sdc_raster object

• mean, extract the mean layer from a sdc_raster object

References

de Jonge, E., & de Wolf, P. P. (2016, September). Spatial smoothing and statistical disclosure control. In International Conference on Privacy in Statistical Databases (pp. 107-117). Springer, Cham.

de Wolf, P. P., & de Jonge, E. (2018, September). Safely Plotting Continuous Variables on a Map. In International Conference on Privacy in Statistical Databases (pp. 347-359). Springer, Cham.

Suñé, E., Rovira, C., Ibáñez, D., Farré, M. (2017). Statistical disclosure control on visualising geocoded population data using a structure in quadtrees, NTTS 2017

See also

Useful links:

• https://github.com/edwindj/sdcSpatial

• Report bugs at https://github.com/edwindj/sdcSpatial/issues

Author

Maintainer: Edwin de Jonge edwindjonge@gmail.com (ORCID)

Authors:

• Peter-Paul de Wolf

Other contributors:

• Douwe Hut [contributor]

• Sapphire Han [contributor]