保护隐私的前提下，安全链接公私农业数据，助力政策研究。
Privacy-Preserving Data Linkage Across Private and Public Datasets for Collaborative Agriculture Research
- 基于隐私保护算法匹配相似农户并聚合信息
- 通过机器学习模型验证框架有效性，支持政策分析
- 适合关注农业数据安全与政策研究的团队
数字农业利用技术提升作物产量、抗病能力和土壤健康，在农业研究中至关重要。然而，其引发的隐私风险如价格歧视、保险成本上升和资源操纵，使农场主不愿共享数据。本研究提出一种隐私保护框架，解决这些风险的同时实现安全数据共享。该框架可全面分析数据并保护隐私，使利益相关方能整合公共与私有数据，开展研究驱动型政策分析。算法通过：(1) 基于私有数据识别相似农户；(2) 提供时间与位置等聚合信息；(3) 分析价格与产品供应趋势；(4) 关联趋势与公共政策数据（如粮食不安全统计）。我们使用真实世界农民市场数据集验证框架，通过在隐私保护数据上训练的机器学习模型证明其有效性。结果支持政策制定者和研究人员应对粮食不安全与定价问题。本工作为数字农业提供了一种安全的数据融合与分析方法，推动农业技术创新与发展。
原文摘要 · Abstract (English)
Digital agriculture leverages technology to enhance crop yield, disease resilience, and soil health, playing a critical role in agricultural research. However, it raises privacy concerns such as adverse pricing, price discrimination, higher insurance costs, and manipulation of resources, deterring farm operators from sharing data due to potential misuse. This study introduces a privacy-preserving framework that addresses these risks while allowing secure data sharing for digital agriculture. Our framework enables comprehensive data analysis while protecting privacy. It allows stakeholders to harness research-driven policies that link public and private datasets. The proposed algorithm achieves this by: (1) identifying similar farmers based on private datasets, (2) providing aggregate information like time and location, (3) determining trends in price and product availability, and (4) correlating trends with public policy data, such as food insecurity statistics. We validate the framework with real-world Farmer's Market datasets, demonstrating its efficacy through machine learning models trained on linked privacy-preserved data. The results support policymakers and researchers in addressing food insecurity and pricing issues. This work significantly contributes to digital agriculture by providing a secure method for integrating and analyzing data, driving advancements in agricultural technology and development.
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