A comparison of obfuscation methods used for privacy protection: Exploring the challenges of polygon data in agricultural research. Issue 2 (6th January 2022) - Nowbakht, Parvaneh; O'Sullivan, Lilian; Cawkwell, Fiona; Wall, David P.; Holloway, Paul
Abstract: 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.