The Annals of Applied Statistics

Multiple imputation for sharing precise geographies in public use data

Hao Wang and Jerome P. Reiter

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When releasing data to the public, data stewards are ethically and often legally obligated to protect the confidentiality of data subjects’ identities and sensitive attributes. They also strive to release data that are informative for a wide range of secondary analyses. Achieving both objectives is particularly challenging when data stewards seek to release highly resolved geographical information. We present an approach for protecting the confidentiality of data with geographic identifiers based on multiple imputation. The basic idea is to convert geography to latitude and longitude, estimate a bivariate response model conditional on attributes, and simulate new latitude and longitude values from these models. We illustrate the proposed methods using data describing causes of death in Durham, North Carolina. In the context of the application, we present a straightforward tool for generating simulated geographies and attributes based on regression trees, and we present methods for assessing disclosure risks with such simulated data.

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Ann. Appl. Stat., Volume 6, Number 1 (2012), 229-252.

First available in Project Euclid: 6 March 2012

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Confidentiality disclosure dissemination spatial synthetic tree


Wang, Hao; Reiter, Jerome P. Multiple imputation for sharing precise geographies in public use data. Ann. Appl. Stat. 6 (2012), no. 1, 229--252. doi:10.1214/11-AOAS506.

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Supplemental materials

  • Supplementary material: Computational details and further results. Computational details for geography disclosure and identification risks in Sections 3.2.1 and 3.2.2; further analytical validity results; and results based on genuine cause of death.