Decentralized, secure and privacy-preserving sharing of health data: A survey and future directions

Secure and privacy-preserving sharing of health data is a critical requirement in modern and future healthcare systems. Centralized architectures often fall short due to their inherent risks, including lack of transparency and limited user control. This survey examines the ongoing shift from centralized health data platforms to decentralized, secure, and privacy-preserving health data sharing, driven by the rapid growth of electronic health records (EHRs) and the Internet of Medical Things (IoMT) ecosystems. We categorize the state-of-the-art approaches into frameworks for data sharing, auditability, and access control; frameworks for privacy-preserving analytics and learning; and frameworks for decentralized identity, consent, and credential management. For each category, we analyze representative systems in depth, outlining their architecture, security mechanisms, and their limitations. We analyze how enabling technologies, such as distributed ledgers, smart contracts, decentralized storage, Privacy-Enhancing Technologies (PETs), and privacy-preserving analytics and learning mechanisms, can be combined to achieve confidentiality and integrity without centralizing raw patient data. Beyond comparing representative systems, the survey identifies key adoption barriers, including computational and communication overhead, interoperability with legacy infrastructures, usability, key management, governance and trust bootstrapping, and regulation-aligned consent and revocation. The paper concludes by identifying open research gaps and outlining actionable future directions for developing deployable decentralized health-data ecosystems.