Research Article
June 2004

Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?

Publication: Cartographica: The International Journal for Geographic Information and Geovisualization
Volume 39, Number 2

Abstract

Spatial analysis and mapping of georeferenced, individual-level data can help identify important geographical patterns or lead to knowledge significant for dealing with specific social issues in a particular area. However, given the need to protect personal privacy when using geospatial data, the possibility for undertaking geographical analysis on certain types of individual-level data is becoming increasingly circumscribed. This article addresses the need to protect geoprivacy while making georeferenced, individual-level data available in such a way that analytical results are not significantly affected. The effectiveness of three geographical masks with different perturbation radii (r) is examined using a data set for lung-cancer deaths in Franklin County, Ohio, in 1999. The findings reveal a rather consistent trade-off between data confidentiality and accuracy of analytical results. There seems to be a threshold r-value at which the results of analyses on masked data become substantially different from the original results. An r that produces an area about the average size of the study-area census-block groups achieves a desirable optimum trade-off between privacy protection and accuracy of results. The study shows that implementing appropriate geographical masks may help data managers or researchers establish the desirable trade-off, in a particular context, between privacy protection and accuracy of geographic information.

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Information & Authors

Information

Published In

Go to Cartographica
Cartographica: The International Journal for Geographic Information and Geovisualization
Volume 39Number 2June 2004
Pages: 15 - 28

History

Published in print: June 2004
Published online: 19 September 2006

Keywords

  1. geoprivacy
  2. privacy
  3. accuracy
  4. geographical masks
  5. disaggregate data
  6. lung cancer deaths

Authors

Affiliations

Mei-Po Kwan
Department of Geography, The Ohio State University, Columbus, OH, USA
Irene Casas
Department of Geography, University at Buffalo-SUNY, Buffalo, NY, USA
Ben Schmitz
Environmental Systems Research Institute, Inc., Danvers, MA, USA

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Mei-Po Kwan, Irene Casas, and Ben Schmitz
Cartographica 2004 39:2, 15-28

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