Secure Curation and Reuse of Digital Scientific Data Using Retrieval-Augmented Generation Systems

This study explores secure and intelligent curation of digital scientific data using Retrieval-Augmented Generation (RAG) systems. Focusing on privacy-preserving approaches and reuse of structured healthcare datasets, we propose models for fraud detection, enhancing semantic interpretation, personalization, and secure access to digital knowledge assets in critical sectors.