数字农业沙箱：面向协作研究的平台 (Digital Agriculture Sandbox for Collaborative Research)

from arxiv, Presents a privacy-preserving digital agriculture platform using federated learning, differential privacy, and secure data analysis to enable collaboration between farmers and researchers without exposing raw data. Demonstrates secure similarity search, model training, and risk-aware data sharing

Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive. This modern approach to agriculture generates a wealth of valuable data that could help address global food challenges, but farmers are hesitant to share it due to privacy concerns. This limits the extent to which researchers can learn from this data to inform improvements in farming. This paper presents the Digital Agriculture Sandbox, a secure online platform that solves this problem. The platform enables farmers (with limited technical resources) and researchers to collaborate on analyzing farm data without exposing private information. We employ specialized techniques such as federated learning, differential privacy, and data analysis methods to safeguard the data while maintaining its utility for research purposes. The system enables farmers to identify similar farmers in a simplified manner without needing extensive technical knowledge or access to computational resources. Similarly, it enables researchers to learn from the data and build helpful tools without the sensitive information ever leaving the farmer's system. This creates a safe space where farmers feel comfortable sharing data, allowing researchers to make important discoveries. Our platform helps bridge the gap between maintaining farm data privacy and utilizing that data to address critical food and farming challenges worldwide.

翻译：数字农业正通过技术应用改变粮食生产方式，使农业更高效、可持续且高产。这种现代农业方法产生了大量宝贵数据，有助于应对全球粮食挑战，但农户因隐私顾虑不愿共享数据，限制了研究人员利用这些数据推动农业改进的程度。本文提出数字农业沙箱——一个解决此问题的安全在线平台。该平台使技术资源有限的农户与研究人员能在不暴露隐私信息的前提下协作分析农业数据。我们采用联邦学习、差分隐私及数据分析方法等专业技术，在保护数据的同时保持其研究价值。系统允许农户以简化的方式识别相似农户，无需深厚技术知识或计算资源；同样使研究人员能在敏感数据不离开农户系统的前提下学习数据并构建实用工具。这创造了一个让农户安心共享数据的安全空间，助力研究人员实现重要发现。我们的平台有助于弥合农业数据隐私保护与利用数据应对全球粮食及农业挑战之间的鸿沟。