From Privacy-Utility Trade-Offs to Policies: Optimized Anonymization Recommendations for Data Trustees in Data Spaces

Abstract

: As data spaces emerge to facilitate sovereign data exchange, geospatial data has become a critical resource across various domains. However, sharing sensitive geodata, such as cadastral property records, presents a privacy-utility trade-off. While the European Data Governance Act (DGA) establishes data trustees as the technical intermediaries authorized to perform necessary anonymization, a gap remains between theoretical spatial privacy algorithms and their practical, policy-driven application. This research addresses this gap by presenting an empirical evaluation of 11 spatial anonymization methods applied to complex polygon geometries. Using a dataset of 2,147 forested cadastral parcels, we quantify the trade-offs using privacy metrics ( k -anonymity, differential privacy ε ) and utility measures (Hausdorff distance, area deviation, centroid shift). Our results identify algorithmic failure modes, such as the “sparse forest” phenomenon, and demonstrate the improved performance of combined hybrid approaches. To operationalize these findings, we integrate our results into the architecture of data trustees operating within various domain-specific data spaces. We demonstrate how the empirically derived, use-case-specific anonymization guidelines can be translated into machine-interpretable Open Digital Rights Language (ODRL) policies. By mapping spatial transformation algorithms to ODRL constraints, this research provides data trustees with an approach to automatically enforce spatial privacy and sovereignty within data spaces.