A smoothing approach for masking spatial data

Yijie Zhou, Francesca Dominici, Thomas A. Louis

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1451-1475
Number of pages25
JournalAnnals of Applied Statistics
Issue number3
StatePublished - Sep 2010
Externally publishedYes


  • Data masking
  • Data utility
  • Disclosure risk
  • Spatial smoothing
  • Statistical disclosure limitation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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