The verified neighbor approach to geoprivacy: An improved method for geographic masking

Abstract

Geographic information adds a powerful component to environmental epidemiology studies but can compromise subject confidentiality. Although locations are often masked by perturbing spatial coordinates, existing masks do not ensure that the perturbation area contains a sufficient number of valid surrogates to prevent disclosure, nor are they designed to minimize perturbation while maintaining a specified level of privacy. I introduce a new approach to geoprivacy in which real property parcel data with information about land use are used to develop a pool of verified neighbors. GIS (geographic information system) processing optionally restricts the pool to residences with values of environmental variables similar to those of the subject parcel. A surrogate is then randomly selected from the k members of the pool closest to the subject with k chosen to achieve the desired spatial privacy protection. The method guarantees the specified level of privacy even where population density is uneven while minimizing spatial distortion and changes to the values of environmental variables assigned to subjects. The method is illustrated with an example that found it to be more effective than random perturbation-based methods in both protecting privacy and preserving spatial fidelity to the original locations.