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Geographically intelligent disclosure control for flexible aggregation of census data

Geographically intelligent disclosure control for flexible aggregation of census data
Geographically intelligent disclosure control for flexible aggregation of census data
This paper describes a geographically intelligent approach to disclosure control for protecting flexibly aggregated census data. Increased analytical power has stimulated user demand for more detailed information for smaller geographical areas and customized boundaries. Consequently it is vital that improved methods of statistical disclosure control are developed to protect against the increased disclosure risk. Traditionally methods of statistical disclosure control have been aspatial in nature. Here we present a geographically intelligent approach that takes into account the spatial distribution of risk. We describe empirical work illustrating how the flexibility of this new method, called local density swapping, is an improved alternative to random record swapping in terms of risk-utility.
privacy, census data, spatial analysis, small area geography, quality issues
1365-8816
457-482
Young, Caroline
1130f937-8c1d-4fba-9b2b-0488881dc384
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Skinner, Chris
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
Young, Caroline
1130f937-8c1d-4fba-9b2b-0488881dc384
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Skinner, Chris
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce

Young, Caroline, Martin, David and Skinner, Chris (2009) Geographically intelligent disclosure control for flexible aggregation of census data. International Journal of Geographical Information Science, 23 (4), 457-482. (doi:10.1080/13658810801949835).

Record type: Article

Abstract

This paper describes a geographically intelligent approach to disclosure control for protecting flexibly aggregated census data. Increased analytical power has stimulated user demand for more detailed information for smaller geographical areas and customized boundaries. Consequently it is vital that improved methods of statistical disclosure control are developed to protect against the increased disclosure risk. Traditionally methods of statistical disclosure control have been aspatial in nature. Here we present a geographically intelligent approach that takes into account the spatial distribution of risk. We describe empirical work illustrating how the flexibility of this new method, called local density swapping, is an improved alternative to random record swapping in terms of risk-utility.

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More information

Published date: April 2009
Keywords: privacy, census data, spatial analysis, small area geography, quality issues
Organisations: PHEW – S (Spatial analysis and modelling), Remote Sensing & Spatial Analysis, Social Statistics

Identifiers

Local EPrints ID: 66769
URI: http://eprints.soton.ac.uk/id/eprint/66769
ISSN: 1365-8816
PURE UUID: 4adb8f06-d016-46f8-8090-974c6a482f59
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 20 Jul 2009
Last modified: 14 Mar 2024 02:36

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Contributors

Author: Caroline Young
Author: David Martin ORCID iD
Author: Chris Skinner

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