License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2021.II.1
URN: urn:nbn:de:0030-drops-147606
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14760/
Go to the corresponding LIPIcs Volume Portal

Polzin, Fiona ; Kounadi, Ourania

Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data

pdf-format:
LIPIcs-GIScience-2021-II-1.pdf (5 MB)

Abstract

Geomasks assure the protection of individuals in a discrete spatial point data set by aggregating, transferring or altering original points. This study develops an alternative approach, referred to as Adaptive Voronoi Masking (AVM), which is based on the concepts of Adaptive Aerial Elimination (AAE) and Voronoi Masking (VM). It considers the underlying population density by establishing areas of K-anonymity in which Voronoi polygons are created. Contrary to other geomasks, AVM considers the underlying topography and displaces data points to street intersections thus decreasing the risk of false-identification since residences are not endowed with a data point.
The geomasking effects of AVM are examined by various spatial analytical results and are compared with the outputs of AAE, VM, and Donut Masking (DM). VM attains the best efficiency for the mean centres whereas DM does for the median centres. Regarding the Nearest Neighbour Hierarchical Cluster Analysis and Ripley’s K-function, DM demonstrates the strongest performance since its cluster ellipsoids and clustering distance are the most similar to those of the original data. The extend of the original data is preserved the most by VM, while AVM retains the topology of the point pattern. Overall, AVM was ranked as 2nd in terms of data utility (i) and also outperforms all methods regarding the risk of false re-identification (ii) because no data point is moved to a residence. Furthermore, AVM maintains the Spatial K-anonymity (iii) which is also done by AAE and partly by DM. Based on the performance combination of these factors, AVM is an advantageous technique to mask geodata.

BibTeX - Entry

@InProceedings{polzin_et_al:LIPIcs.GIScience.2021.II.1,
  author =	{Polzin, Fiona and Kounadi, Ourania},
  title =	{{Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{1:1--1:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14760},
  URN =		{urn:nbn:de:0030-drops-147606},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.1},
  annote =	{Keywords: Geoprivacy, location privacy, geomasking, Adaptive Voronoi Masking, Voronoi Masking, Adaptive Aerial Elimination, Donut Geomasking, ESDA}
}

Keywords: Geoprivacy, location privacy, geomasking, Adaptive Voronoi Masking, Voronoi Masking, Adaptive Aerial Elimination, Donut Geomasking, ESDA
Collection: 11th International Conference on Geographic Information Science (GIScience 2021) - Part II
Issue Date: 2021
Date of publication: 14.09.2021
Supplementary Material: The Geoprivacy Github repository contains the scripts to run AVM (Also AAE) as well as one of the six area data sets used in this study:
Software: https://github.com/okounadi/Geoprivacy

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI