Implementation of novel polygon-based obfuscation methods to improve privacy of agricultural data

Geoprivacy protection is a significant concern when sharing data. To support sustainable land management by leveraging existing agricultural data, research is needed to identify how the polygon nature of static field parcels can be obfuscated to allow data sharing among individuals and organizations. In this study, five adaptive polygon-based obfuscation methods including PN*Rand, PDonut-k, PDensity-k, PAHilb, and PDonut_AHilb methods were developed and applied on the Irish Nutrient Management Planning Online (NMP Online) agricultural dataset. The polygon-based obfuscation methods introduced in this study were designed with the consideration of properties of spatial polygon objects including the spatial coordinates, shape and size of the polygon, topology, and spatial relationship between adjacent polygons that can be used to identify real-world objects. These methods were developed to guarantee that there is no false-identification and non-unique obfuscation which is important for static polygon objects in terms of accuracy and privacy protection. Qualitative approaches were developed to identify the optimal values of inner and outer radii of donut shape based on k-anonymity satisfaction and subsequently obtain the optimal value of k-anonymity. Several evaluation methods were implemented to compare the methods performance. Density-based methods particularly PDonut-AHilb provide the best trade-off between field parcel confidentiality and spatial pattern preservation and should be considered for researchers and practitioners obfuscating polygon data.