Rosemarie Santa González

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.