ArticleSensors (Basel, Switzerland)2022
A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records.Frontiers in public health · 2023Pooled it
- Secure Fog Computing for Remote Health Monitoring with Data Prioritisation and AI-Based Anomaly Detection.Sensors (Basel, Switzerland) · 2025Article
- Dual attention-based deep learning with blockchain for multimedia data processing and secure access control in IoHT.Scientific reports · 2025Article
- Quantum resilient security framework for privacy preserving AI in Apple MM1 on device architecture.Scientific reports · 2025Article
- Secure and fault tolerant cloud based framework for medical image storage and retrieval in a distributed environment.Scientific reports · 2025Article
- An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things.Scientific reports · 2025Article
- A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things.Sensors (Basel, Switzerland) · 2023Review
- Cyber-Internet Security Framework to Conquer Energy-Related Attacks on the Internet of Things with Machine Learning Techniques.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.