Geospatial data obfuscation methods applied to agricultural data

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Date
2023
Authors
Nowbakht, Parvaneh
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University College Cork
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Abstract
Geoprivacy protection is a controversial subject within the field of agri-environmental research. Agricultural transformation and digital farming are widely used in the agriculture industry. This enhances sustainable agriculture and subsequently sustainable development and food security. At the same time, such technological advances can mitigate climate change by increasing agricultural productivity while protecting natural resources and reducing the environmental impact of agricultural activities. Digital farming generates a high volume of data including spatial data. This data can be used in data-driven modelling to improve agriculture systems and design and develop more sustainable agricultural policies and services to enhance sustainable agriculture. Sharing and making data accessible to a wide range of researchers and stakeholders is essential to gain maximum use of data. Data privacy and particularly geoprivacy protection is an essential factor when sharing agricultural data and needs urgent investigation. To date, point-based obfuscation methods are the predominant approaches used to protect object confidentiality with spatial pattern and statistical accuracy preservation. In this research, functionality of point-based obfuscation methods for polygon nature objects was investigated. The terms “non-unique obfuscation” was introduced to the scientific literature for the first time. A high percentage of false-identification and non-unique obfuscation was recognized as the main drawback of point-based obfuscation when applied on polygon centroids as point spatial data. Agricultural spatial data is often best represented as static polygon data with an association of attributes/properties of a field parcel or farm that includes coordinates, shape, size, topology, the relationship with the surrounding environment, and the impact of external factors on region characteristics that can be used to breach privacy. Agricultural spatial data with unique characteristics that distinguish them from other spatial data, therefore, require different geoprivacy protection techniques. Therefore, for the first time, to achieve a high level of geoprivacy protection, several polygon-based obfuscation methods including PN*Rand, PDonut-k, PDensity-k, PAHilb, PDonut_AHilb and PESOM methods were developed with consideration of these properties and avoidance of the occurrence of false-identification and non-unique obfuscation. The comparison of the performance of obfuscation methods, both point-based and polygon-based, in different aspects using various evaluation metrics indicated that the density-based obfuscation methods provided a better trade-off between level of confidentiality and accuracy. The results demonstrated that PESOM method maintained a high level of geoprivacy protection and absolute environmental and climatic clustering preservation with no false identification and non-unique obfuscation risk. Following on from this. a case study was conducted to showcase the useability of obfuscated data to solve real world problems and highlight the importance of the choice of obfuscation method in outcome and suitability of obfuscated data for a certain purpose. Several evaluation metrics were developed to examine and assess the performance of obfuscation methods in terms of determining the level of privacy protection and spatial pattern preservation, and statistical accuracy. The results confirm the importance of choosing the right obfuscation method based on the influence of internal and external features on the results of the data-driven model. Therefore, the results of this research should be of wide interest to those working in GIScience, agri-environmental research, and computer science, and be of relevance to researchers and data managers.
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Keywords
Geoprivacy , Obfuscation , Spatial analysis , k-anonymity , Agriculture , Environment
Citation
Nowbakht, P. 2023. Geospatial data obfuscation methods applied to agricultural data. PhD Thesis, University College Cork.