Geographical masking is the conventional solution to protect the privacy of individuals involved in confidential spatial point datasets. The masking process displaces confidential locations to protect individual privacy while maintaining a fine level of spatial resolution. The adaptive form of this process aims to further minimize the displacement error by taking into account the underlying population density. We describe an alternative adaptive geomasking method, referred to as Adaptive Areal Elimination (AAE). AAE creates areas of a minimum K-anonymity and then original points are either randomly perturbed within the areas or aggregated to the median centers of the areas. In addition to the masked points, K-anonymized areas can be safely disclosed as well without increasing the risk of re-identification. Using a burglary dataset from Vienna, AAE is compared with an existing adaptive geographical mask, the donut mask. The masking methods are evaluated for preserving a predefined K-anonymity and the spatial characteristics of the original points. The spatial characteristics are assessed with four measures of spatial error: displaced distance, correlation coefficient of density surfaces, hotspots' divergence, and clusters' specificity. Masked points from point aggregation of AAE have the highest spatial error in all the measures but the displaced distance. In contrast, masked points from the donut mask are displaced the least, preserve the original spatial clusters better, have the highest clusters' specificity and correlation coefficient of density surfaces. However, when the donut mask is adapted to achieve an actual K-anonymity, the random perturbation of AAE introduces less spatial error than the donut mask for all the measures of spatial error. 
Adaptive areal elimination (AAE): A transparent way of disclosing protected spatial datasets