A comparison of obfuscation methods used for privacy protection: Exploring the challenges of polygon data in agricultural research

A major challenge of sharing spatially explicit agricultural and agri-environmental data is to identify the trade-off between field parcel confidentiality and spatial pattern preservation. In this work, 27 point-based obfuscation and evaluation methods were applied on agricultural data, collected by the Irish Nutrient Management Planning Online (NMP Online) platform, which is a high-density polygon dataset developed to inform precision agriculture through nutrient management based on soil fertility and agronomic targets. Broad categorizations of methods—including N*Rand, Donut, Density, Pinwheel, AHilb, and k-anonymity—were developed, combined, and modified to achieve the best trade-off between security and accuracy. To improve geoprivacy and spatial pattern preservation of existing Donut and Density methods, qualitative approaches, including Donut-k and Density-k methods, were introduced which identify the optimal values of radii based on a combination of the Donut method and k-anonymity satisfaction, and subsequently optimal k-anonymity determination. Modified AHilb and Donut-AHilb methods were also developed to generate smaller and arbitrary obfuscation areas to improve location security. The Donut-AHilb method was found to be the best at spatial pattern preservation and satisfying larger k-anonymity, but the risk of false identification and non-unique obfuscation was high when considering the polygon nature of agricultural data such as field parcels. Further, we introduce the term “non-unique obfuscated points,” which is important when obfuscating static objects as two or more points might have the same obfuscated location, which has relevance to the wider GIScience community.