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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.
M07/12
University of Southampton
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 (2007) Geographically intelligent disclosure control for flexible aggregation of census data (S3RI Methodology Working Papers, M07/12) Southampton, GB. University of Southampton 49pp.

Record type: Monograph (Working Paper)

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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Published date: 11 July 2007

Identifiers

Local EPrints ID: 46686
URI: https://eprints.soton.ac.uk/id/eprint/46686
PURE UUID: 2b91080a-9690-4d9d-955f-ee6d77848157
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

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Date deposited: 11 Jul 2007
Last modified: 14 Mar 2019 01:53

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