  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.
