Assessing the protection provided by misclassification-based disclosure limitation methods for survey microdata

Natalie Shlomo and Chris J. Skinner

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Government statistical agencies often apply statistical disclosure limitation techniques to survey microdata to protect the confidentiality of respondents. There is a need for valid and practical ways to assess the protection provided. This paper develops some simple methods for disclosure limitation techniques which perturb the values of categorical identifying variables. The methods are applied in numerical experiments based upon census data from the United Kingdom which are subject to two perturbation techniques: data swapping (random and targeted) and the post randomization method. Some simplifying approximations to the measure of risk are found to work well in capturing the impacts of these techniques. These approximations provide simple extensions of existing risk assessment methods based upon Poisson log-linear models. A numerical experiment is also undertaken to assess the impact of multivariate misclassification with an increasing number of identifying variables. It is found that the misclassification dominates the usual monotone increasing relationship between this number and risk so that the risk eventually declines, implying less sensitivity of risk to choice of identifying variables. The methods developed in this paper may also be used to obtain more realistic assessments of risk which take account of the kinds of measurement and other nonsampling errors commonly arising in surveys.

Keywords: disclosure risk; identification risk; log linear model; measurement error; post randomization method; data swapping (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Annals of Applied Statistics, 2010, 4(3), pp. 1291-1310. ISSN: 1932-6157

Downloads: (external link)
http://eprints.lse.ac.uk/39119/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:39119

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().