Recent years have seen an increase in location privacy research, including the application of geomasking procedures. Geographical masks aim to protect privacy and preserve spatial information through the displacement of point data. False identification, or the mistaken association of data with the incorrect person or household, is an unexplored issue in geomasking, despite legal protections against false association. This study introduces a topological framework for assessing identification risk and examines the risk of false identification in four masking techniques: random perturbation, donut masking, and the newer Voronoi and MGRS masking techniques. While Voronoi masking is found to best preserve the clustering properties of a sample of urban foreclosure data, the other three masking techniques result in better protection against correct and false identification.