IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain
Open accessAbstract
Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters.
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[14] introduced an IoT-based model for secure big data transfer in healthcare, leveraging blockchain for data integrity and security.
“The authors of [14] introduced an IoT-based model for secure big data transfer in healthcare, leveraging blockchain for data integrity and security.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [14]
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Cited by
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Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567 · see citing works
- Authors
- Elhoseny, M., Haseeb, K., Shah, A. A., Ahmad, I., Jan, Z., & Alghamdi, M. I.
- Affiliations
- Mansoura University (EG)
- Islamia College University (PK)
- Bahria University (PK)
- Al Baha University (SA)
- Corresponding author
- Elhoseny, Mohamed
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2021-08-28
- Volume
- 14
- Issue
- 17
- Pages
- 5364
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W3198090163
- Cited by
- 41
- References
- 47
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 14 · 2023: 10 · 2022: 7
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, Internet of Medical Things, privacy preservation, blockchain, big data transfer, artificial intelligence, graph-based modeling, secure data transmission, health data security
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