J. Goseling and M.N.M. van Lieshout's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.

Jasper Goseling, Marie-Colette van Lieshout*

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

48 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)46-47
Number of pages2
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume84
Issue number1
DOIs
Publication statusPublished - 21 Feb 2022

Keywords

  • UT-Hybrid-D

Fingerprint

Dive into the research topics of 'J. Goseling and M.N.M. van Lieshout's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.'. Together they form a unique fingerprint.

Cite this