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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research

, , , ,

DOI: http://dx.doi.org/10.15439/2026F1512

Citation: Osama Zafar, , , ,

Full text

Abstract. Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from sharing data, as it can be used against them. To address this barrier, we propose a privacy-preserving framework that enables secure data sharing and collaboration for research and development while mitigating privacy risks. The framework combines dimensionality reduction techniques and differential privacy by introducing noise to protect sensitive information. The proposed framework allows researchers to identify potential collaborators for a target farmer and to train personalized machine learning models either on the data of identified collaborators via federated learning or directly on aggregated, privacy-protected data. It also allows farmers to identify potential collaborators based on similarities. We have validated this on real-world datasets, demonstrating robust privacy protection against adversarial attacks and utility performance comparable to that of a centralized system. We demonstrate how this framework can facilitate collaboration among farmers and help researchers pursue broader research objectives. The adoption of the framework can empower researchers and policymakers to responsibly leverage agricultural data, paving the way for transformative advances in data-driven agriculture. By addressing critical privacy challenges, this work supports secure data integration, fostering innovation and sustainability in agricultural systems.

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