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Article . 2026
License: CC BY NC ND
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY NC ND
Data sources: Datacite
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Enhancing Health Data Sharing through Blockchain and IPFS Integration with Machine Learning Based Optimization

Authors: OMMANE, Younes; Dr. Benlemlih, Youssef;

Enhancing Health Data Sharing through Blockchain and IPFS Integration with Machine Learning Based Optimization

Abstract

This preprint presents a decentralized framework that integrates blockchain technology with the InterPlanetary File System (IPFS) to enable secure, interoperable sharing of Electronic Health Records (EHRs). We address the limitations of centralized health data systems—privacy risks, security weaknesses, and poor cross-platform compatibility—by proposing a dual-layer architecture: (1) an abstract service layer that unifies data access across heterogeneous blockchain platforms (e.g., Ethereum and Hyperledger Fabric), and (2) a patient-controlled access mechanism leveraging smart contracts and machine-learning–based anomaly detection. Using synthetic datasets, experimental evaluation shows a 15% reduction in data exchange latency and an improvement in transaction success rate (e.g., from 95% to 98%) compared to non-optimized routing. The integration of Random Forest for routing optimization and Isolation Forest for anomaly detection enhances both performance and security, achieving accuracies of 95% and 92%, respectively. Overall, the study demonstrates the feasibility of scalable, secure, and patient-centric health data exchange in a decentralized environment.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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