Health Data Sharing towards Knowledge Creation

Data sharing and service reuse in the health sector pose significant privacy and security challenges. The European Commission recognizes health data as a unique and cost-effective resource for research, while the OECD emphasizes the need for privacy-protecting data governance systems. In this paper, we propose a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. Our framework establishes a secure environment that enforces GDPR adoption. We present an Information Sharing Infrastructure (ISI) framework that seamlessly integrates artificial intelligence (AI) capabilities for data analysis. Through our implementation, we demonstrate the ease of applying AI algorithms to treated health data within the ISI environment. Evaluating machine learning models, we achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. To address privacy concerns, our framework incorporates Data Sharing Agreements (DSAs). Data producers and consumers (prosumers) have the flexibility to express their prefearences for sharing and analytics operations. Data-centric policy enforcement mechanisms ensure compliance and privacy preservation. In summary, our comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics. By fostering collaboration and knowledge creation in a secure environment, our approach contributes to the advancement of medical research and improves healthcare outcomes. A real case application was implemented between Portuguese hospitals and universities for this data sharing.

Automated summary

This paper proposes a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. The framework establishes a secure environment that enforces GDPR adoption and integrates artificial intelligence capabilities for data analysis. Machine learning models achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. The comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics, contributing to the advancement of medical research and healthcare outcomes.