A deep learning-based privacy-preserving model for smart healthcare in internet of medical things using fog computing
Open accessAbstract
With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called δ r sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that δ r sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art.
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Edge computing has already demonstrated active participation of different industrial interests, namely hardware and IT (mDCs and FC) and telecommunications (MEC).
“Industry players: Edge computing has already demonstrated the active participation of different industrial interests, namely hardware and IT (mDCs and FC) and telecommunications (MEC) [31].”
From Edge AI: A survey (2023) · cites this paper as [31]
The Introduction (page unavailable) describes fog computing as an intermediary layer at the network edge, but does not discuss participation by hardware, IT, or telecommunications companies. Searches for industrial interests, mDCs, FC, and MEC found no passage in our copy supporting that claim. (LLM, conf=0.75)
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Self-citation
- Authors
- Moqurrab, S. A., Tariq, N., Anjum, A., Asheralieva, A., Malik, S. U. R., Malik, H., Pervaiz, H., & Gill, S. S.
- Affiliations
- COMSATS University Islamabad (PK)
- Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK)
- Southern University of Science and Technology (CN)
- Cybernetica (Estonia) (EE)
- Edge Hill University (GB)
- Lancaster University (GB)
- Queen Mary University of London (GB)
- Corresponding author
- Gill, Sukhpal Singh
- ORCID
- Venue
- Wireless Personal Communications
- Published
- 2022-08-30
- Volume
- 126
- Issue
- 3
- Pages
- 2379-2401
- ISSN
- 0929-6212 (ISSN-L)
- 1572-834X
- PMC
- PMC9426374
- OpenAlex
- W4293567445
- Cited by
- 65
- References
- 37
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 19 · 2023: 16 · 2022: 1
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Wireless Personal Communications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- Edge Hill University Research Information Repository (Edge Hill University) (acceptedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61950410603)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Internet of Medical Things, fog computing, smart healthcare, medical entity recognition, convolutional neural networks, bidirectional LSTM, health data sharing, utility preservation, real-time data processing
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