Decentralized Health Intelligence Network (DHIN)
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Abstract (arXiv)
Decentralized Health Intelligence Network (DHIN) extends the Decentralized Intelligence Network (DIN) framework to address challenges in healthcare data sovereignty and AI utilization. Building upon DIN's core principles, DHIN introduces healthcare-specific components to tackle data fragmentation across providers and institutions, establishing a sovereign architecture for healthcare provision. It facilitates effective AI utilization by overcoming barriers to accessing diverse health data sources. This comprehensive framework leverages: 1) self-sovereign identity architecture coupled with a personal health record (PHR), extending DIN's personal data stores concept to ensure health data sovereignty; 2) a scalable federated learning (FL) protocol implemented on a public blockchain for decentralized AI training in healthcare, tailored for medical data; and 3) a scalable, trustless rewards mechanism adapted from DIN to incentivize participation in healthcare AI development. DHIN operates on a public blockchain with an immutable record, ensuring that no entity can control access to health data or determine financial benefits. It supports effective AI training while allowing patients to maintain control over their health data, benefit financially, and contribute to a decentralized ecosystem. Unique to DHIN, patients receive rewards in digital wallets as an incentive to opt into the FL protocol, with a long-term roadmap to fund decentralized insurance solutions. This approach introduces a novel, self-financed healthcare model that adapts to individual needs, complements existing systems, and redefines universal coverage, showcasing how DIN principles can transform healthcare data management and AI utilization while empowering patients.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 50 references that are in the map. First posted on arXiv 12 Aug 2024.
Builds on
11 of 50 references.
References · 11
- 2019Towards Federated Learning at Scale: System Designcited by 826
- 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
- 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
- 2020BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learningcited by 12
- 2021Federated Learning at the Network Edge: When Not All Nodes are Created Equalcited by 10
- 2019A blockchain-orchestrated Federated Learning architecture for healthcare consortiacited by 8
- 2018FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Datacited by 7
- 20202CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environmentscited by 6
- 2023Libertas: Privacy-Preserving Collective Computation for Decentralised Personal Data Storescited by 3
- 2022Secret Sharing Sharing For Highly Scalable Secure Aggregationcited by 2
- 2024Decentralized Intelligence Network (DIN)cited by 2
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