The increasing digitization of healthcare systems has led to unprecedented volumes of sensitive patient data being generated across hospitals, clinics, wearable devices, and telemedicine platforms. While this data holds significant potential for improving predictive analytics, personalized treatment, and population health management, it also raises critical concerns regarding privacy, security, and regulatory compliance. Traditional centralized data-sharing models expose healthcare systems to risks such as data breaches, unauthorized access, and single points of failure
This study explores a privacy-aware healthcare data sharing framework that integrates blockchain technology and federated learning to enable secure, decentralized, and privacy-preserving data collaboration. Blockchain provides an immutable, transparent, and auditable ledger for managing access control, consent management, and data provenance. Federated learning enables machine learning models to be trained across distributed healthcare datasets without transferring raw patient data, thereby preserving privacy while maintaining analytical utility
The proposed integration framework combines the strengths of both technologies to address key challenges in healthcare data interoperability, trust management, and regulatory compliance (e.g., HIPAA and GDPR). The study evaluates architectural models, security mechanisms, and communication protocols required for scalable deployment. The findings suggest that blockchain-enabled federated learning can significantly enhance data security, improve trust among stakeholders, and enable collaborative AI-driven healthcare innovation without compromising patient privacy
