Abstract
We congratulate Professors Dong, Roth and Su on their compelling work on Gaussian Differential Privacy.
In official statistics, the p%-rule (Hundepoel et al., 2012) is widely used to protect tabular data. In recent work (Hut et al., 2020) we adapted this concept to thematic maps, for example, of energy consumption per company. Usually such maps are drawn directly from an underlying table that is protected from disclosure. The resulting colour-coded map, however, is, by construction, discretised in regions defined by the cells in the table. These geographic regions are usually large, corresponding, for instance, to municipalities. The resulting protection is very conservative, leading to a map with reduced utility. Therefore, there is a need for smooth thematic maps.
In official statistics, the p%-rule (Hundepoel et al., 2012) is widely used to protect tabular data. In recent work (Hut et al., 2020) we adapted this concept to thematic maps, for example, of energy consumption per company. Usually such maps are drawn directly from an underlying table that is protected from disclosure. The resulting colour-coded map, however, is, by construction, discretised in regions defined by the cells in the table. These geographic regions are usually large, corresponding, for instance, to municipalities. The resulting protection is very conservative, leading to a map with reduced utility. Therefore, there is a need for smooth thematic maps.
Original language | English |
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Pages (from-to) | 46-47 |
Number of pages | 2 |
Journal | Journal of the Royal Statistical Society. Series B: Statistical Methodology |
Volume | 84 |
Issue number | 1 |
DOIs |
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Publication status | Published - 21 Feb 2022 |
Keywords
- UT-Hybrid-D