Considering Risk Locations When Defining Perturbation Zones for Geomasking

Geomasking techniques are commonly used to mask the true location information of cases by introducing noise into location data. This study seeks to improve the spatially adaptive random perturbation (SARP) geomasking method by using the actual distribution of the residential addresses (or “risk location”) rather than the people (or “risk population”) to define a perturbation zone. The procedure used in the study also employs a “donut-shaped” perturbation zone, rather than the traditional “pancake-shaped” zone, when displacing a case. The effectiveness of the proposed geomasking methods is assessed in terms of their potential to control for location re-engineering and their ability to maintain the point patterns embedded in the real distribution. The authors conclude that SARP geomasking using the distribution of actual street addresses protects privacy more effectively than geomasking based on population size; the different SARP techniques do not significantly change the clustering patterns on a global level, but the geomasked data tend to be more clustered than the real case distribution.