Expert Meeting on Statistical Data Confidentiality

The demand for georeferenced data is increasing, while sharing proprietary location data poses privacy and confidentiality challenges. This study investigates the use of synthetic data generators (SDGs) to protect sensitive locations in georeferenced datasets. We propose transforming spatial coordinates into a one-dimensional index via a Hilbert space-filling curve, thereby preserving local spatial relationships while enabling conventional synthesizers (e.g., synthpop) to synthesize the condensed representation. We evaluate this approach against direct synthesis of latitude and longitude as numeric variables, using disclosure risk measures (re-identification rate) and spatial utility metrics (Average Nearest Neighbor (ANN) index and join count statistic). In a use case with simulated georeferenced health data related to sleep disorders, the Hilbert-based approach underperformed the direct synthesis baseline with respect to both re-identification risk and preservation of spatial relationships. We discuss possible causes for this result and outline limitations of Hilbert-based mapping.