Open Access
September 2010 A smoothing approach for masking spatial data
Yijie Zhou, Francesca Dominici, Thomas A. Louis
Ann. Appl. Stat. 4(3): 1451-1475 (September 2010). DOI: 10.1214/09-AOAS325


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.


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Yijie Zhou. Francesca Dominici. Thomas A. Louis. "A smoothing approach for masking spatial data." Ann. Appl. Stat. 4 (3) 1451 - 1475, September 2010.


Published: September 2010
First available in Project Euclid: 18 October 2010

zbMATH: 1202.62167
MathSciNet: MR2758336
Digital Object Identifier: 10.1214/09-AOAS325

Keywords: data masking , data utility , Disclosure risk , spatial smoothing , Statistical disclosure limitation

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.4 • No. 3 • September 2010
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