A context-sensitive approach to anonymizing spatial surveillance data: impact on outbreak detection.
The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic.
Author(s): Cassa, Christopher A, Grannis, Shaun J, Overhage, J Marc, Mandl, Kenneth D
DOI: 10.1197/jamia.M1920