The Annals of Applied Statistics

A smoothing approach for masking spatial data

Yijie Zhou, Francesca Dominici, and Thomas A. Louis

Full-text: Open access

Abstract

Individual-level health data are often not publicly available due to confidentiality; masked data are released instead. Therefore, it is important to evaluate the utility of using the masked data in statistical analyses such as regression. In this paper we propose a data masking method which is based on spatial smoothing techniques. The proposed method allows for selecting both the form and the degree of masking, thus resulting in a large degree of flexibility. We investigate the utility of the masked data sets in terms of the mean square error (MSE) of regression parameter estimates when fitting a Generalized Linear Model (GLM) to the masked data. We also show that incorporating prior knowledge on the spatial pattern of the exposure into the data masking may reduce the bias and MSE of the parameter estimates. By evaluating both utility and disclosure risk as functions of the form and the degree of masking, our method produces a risk-utility profile which can facilitate the selection of masking parameters. We apply the method to a study of racial disparities in mortality rates using data on more than 4 million Medicare enrollees residing in 2095 zip codes in the Northeast region of the United States.

Article information

Source
Ann. Appl. Stat., Volume 4, Number 3 (2010), 1451-1475.

Dates
First available in Project Euclid: 18 October 2010

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1287409381

Digital Object Identifier
doi:10.1214/09-AOAS325

Mathematical Reviews number (MathSciNet)
MR2758336

Zentralblatt MATH identifier
1202.62167

Keywords
Statistical disclosure limitation data masking data utility disclosure risk spatial smoothing

Citation

Zhou, Yijie; Dominici, Francesca; Louis, Thomas A. A smoothing approach for masking spatial data. Ann. Appl. Stat. 4 (2010), no. 3, 1451--1475. doi:10.1214/09-AOAS325. https://projecteuclid.org/euclid.aoas/1287409381


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Supplemental materials

  • Supplementary material: R code. We provide the R code for (1) the simulation study utility part of the three examples, (2) the function to compute the disclosure risk, and (3) the calculation of the bivariate normal density kernel weight matrix.