Secure Privacy-preserving Resolution Adaptive Data Sharing in Hybrid Blockchain Controlled Medical IoT Environment

Medical Internet of Things (IoT) will revolutionize healthcare industry by facilitating on demand medical assistance.Data collected from various sensor devices attached to patients are transmitted to various application servers which can monitor health and offer various medical assistance services.In this medical IoT era, to ensure data security and privacy of patients, it is necessary to mutually authenticate the communicating entities and deliver data at various resolutions between the entities based on the utilities.This work proposes a hybrid block chain controlled Medical IoT environment (HB-MIOT) for secure, privacy preserving data sharing from IoT devices.The solution proposes a novel multi resolution adaptive data sharing scheme using compressive sensing after consensus agreement between the IoT entities with combined block chain and hybrid cloud.The solution is secure against data leakage, data tampering and data compromise at public clouds.Through simulations, the computation complexity for data upload(encryption) and data retrieval (decryption) is 9% and 3.5% lower in proposed solution compared to most recent incremental update solution.Also, the block chain update cost is 13 times lower in proposed solution.Through experimental analysis with U.S Health dataset, the computation complexity for data upload and data retrieval is 10.3% and 3.2% lower in proposed solution compared to recent privacy-preserving and access control scheme for sharing EHRs using blockchain technology.

Publication Details

Journal
International journal of intelligent engineering and systems
Published
2024-12-25
DOI
https://doi.org/10.22266/ijies2025.0229.89
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

Secure Privacy-preserving Resolution Adaptive Data Sharing in Hybrid Blockchain Controlled Medical IoT Environment

International journal of intelligent engineering and systems
Blockchain Technology Applications and Security
article

Secure Privacy-preserving Resolution Adaptive Data Sharing in Hybrid Blockchain Controlled Medical IoT Environment

article en

Abstract

Medical Internet of Things (IoT) will revolutionize healthcare industry by facilitating on demand medical assistance.Data collected from various sensor devices attached to patients are transmitted to various application servers which can monitor health and offer various medical assistance services.In this medical IoT era, to ensure data security and privacy of patients, it is necessary to mutually authenticate the communicating entities and deliver data at various resolutions between the entities based on the utilities.This work proposes a hybrid block chain controlled Medical IoT environment (HB-MIOT) for secure, privacy preserving data sharing from IoT devices.The solution proposes a novel multi resolution adaptive data sharing scheme using compressive sensing after consensus agreement between the IoT entities with combined block chain and hybrid cloud.The solution is secure against data leakage, data tampering and data compromise at public clouds.Through simulations, the computation complexity for data upload(encryption) and data retrieval (decryption) is 9% and 3.5% lower in proposed solution compared to most recent incremental update solution.Also, the block chain update cost is 13 times lower in proposed solution.Through experimental analysis with U.S Health dataset, the computation complexity for data upload and data retrieval is 10.3% and 3.2% lower in proposed solution compared to recent privacy-preserving and access control scheme for sharing EHRs using blockchain technology.

International journal of intelligent engineering and systemsVol. 18(1)
Openalex Percentile: Top 62%
Blockchain Technology Applications and Security
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Secure Privacy-preserving Resolution Adaptive Data Sharing in Hybrid Blockchain Controlled Medical IoT Environment · International journal of intelligent engineering and systems (2024) | TGRS Research Map | TGRS