Security
Dual attention-based deep learning with blockchain for multimedia data processing and secure access control in IoHT
Authored by: G. Karthik Reddy, Nageswara Rao Lavuri, Shabana Urooj, Krishna Dharavath, Nidal Nasser, Yogapriya J
The Internet of Medical Things (IoMT) represents an interconnected medical technology that comprises mobile applications, medical services, as well as networks. These medical equipment and software are connected to medical systems across an internet connection. Security and confidentiality of healthcare information, flexibility, and data availability are the most complicated IoT problems to tackle. The utilization of multimedia in medical systems permits the collection, processing, and delivery of clinical data in many different types of styles, comprising texts, images, and speech, throughout the web via different effective components. Yet, processing huge quantities of information, such as every individual's findings and images, demands additional human labour and represents safety risks. The fundamental architecture characteristics of blockchain systems including robust data encryption and strong peer-to-peer systems are beneficial and affordable options for addressing a few of these demands. Similarly, Blockchain-aided devices are effective in the field of medical science, due to their resource distribution and verification processes that enable access to information. Current investigations struggle to understand the rising need for enhanced data integration over distinct clinical services and platforms, which results in the evolution of application-centric approaches to patient-centric applications. Therefore, this work presented the efficient multimedia data processing in Internet of Healthcare Things (IoHT) based on Blockchain technology. For the improvement of healthcare resource distribution and to avoid the risk in IoHT, efficient blockchain technology is initiated to manage the security control in a real-time system. Further, the access control is managed by developing the advanced model called Dual Attention-based Deep Bayesian Network (DA-DBN). The developed system provides the secured framework for the multimedia content in IoHT using blockchain technology and DA-DBN by generating a hash of each data. This process helps in determining the changes or alterations in the blockchain. Finally, the performance of the proposed scheme concerning the blockchain is validated against the conventional approach. The result demonstrates the potential of the proposed system in securing the IoHT-based health management. The outcomes reveal that the proposed DA-DBN achieved 23.88% faster access control than SVM leading to improved efficiency and enhanced security. Therefore, the proposed model is well suited for multimedia data processing and secure access to the IoHT applications.
Executive Impact
This analysis details a novel Dual Attention-based Deep Bayesian Network (DA-DBN) integrated with blockchain technology for secure multimedia data processing and access control in Internet of Healthcare Things (IoHT) applications. The model significantly improves data privacy, confidentiality, and accessibility, achieving 23.88% faster access control than SVM and a 95.97% security level. By generating unique hashes for each data piece and leveraging dual attention for precise user authentication, the DA-DBN ensures data integrity and prevents unauthorized access. This framework is particularly well-suited for handling the complex and sensitive nature of healthcare data, providing a robust solution for real-time security management and regulatory compliance in IoHT environments.
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Faster Access Control than SVM
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IoHT Data Security Level
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Energy Consumption (Mean)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The core of the proposed solution is its use of blockchain technology to ensure unparalleled data privacy and integrity for multimedia healthcare data. Each transaction and data modification is recorded on a decentralized ledger, secured with cryptographic hashes. This ensures immutability, preventing unauthorized alterations and providing an intelligent traceability system. The DA-DBN model further enhances this by preventing unauthenticated access, safeguarding sensitive patient information against cyber threats and data breaches.
A critical innovation is the Dual Attention-based Deep Bayesian Network (DA-DBN) for robust access control. This model processes user attributes, device characteristics, and real-time context to make precise authentication decisions. The dual attention mechanism allows the network to focus on both global dependencies and local contextual features, significantly improving accuracy in differentiating legitimate users from potential attackers. This dynamic approach adapts to changing trust levels, making it highly effective against sophisticated threats like MITM and Sybil attacks.
The framework efficiently handles diverse multimedia inputs—text, images, audio, and video—common in IoHT. Traditional systems often struggle with the heterogeneity and volume of such data. Our solution integrates blockchain for secure storage and the DA-DBN for intelligent processing, ensuring that large quantities of clinical data are managed with high quality and minimal human intervention. This leads to improved data management and reduced safety risks, vital for advanced healthcare applications.
Improved Access Control Speed
23.88% The proposed DA-DBN model achieved 23.88% faster access control than SVM, leading to improved efficiency and enhanced security for multimedia data processing in IoHT applications.High Security in IoHT
95.97% The DA-DBN model provided the highest level of security of 95.97% in the IoHT network, indicating higher data security.Enterprise Process Flow
Multimedia Data (Healthcare data of the victims)
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Blockchain Storage
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Multimedia data stored on the blockchain
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User attributes for Authentication analysis
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Developed DA-DBN for User Authentication & Access Control
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Dual Attention mechanism
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DBN
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Unauthenticated user (not allowed for data access)
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Authenticated user (allowed to access to data stored on the blockchain)
| Metric | SVM | LSTM | DNN | DBN | DA-DBN (Proposed) |
|---|---|---|---|---|---|
| Accuracy (Epoch 20) | 81.6% | 85.2% | 74.4% | 88.8% | 98.8% |
| F1-Score (Epoch 20) | NA | NA | NA | NA | 99% |
| Cost per Transaction (Best) | High | High | High | High | Lower |
| Security (Best) | 61% | 59.37% | 71.32% | 44.28% | 95.97% |
| Data Processing Speed | 27.4s | 29.7682s | 25.72s | 24.236s | 23.88s |
| Note: The DA-DBN model consistently outperforms traditional approaches in accuracy, F1-score, cost-efficiency, security, and data processing speed, demonstrating its superior capability for secure multimedia data processing and access control in IoHT. | |||||
Regulatory Compliance & Scalability in Healthcare
The proposed DA-DBN model ensures adherence to regulations like HIPAA and GDPR, providing robust access management, tamper-evident, and verifiable logging. It manages privacy rights and provides key revocation for access control, confirming that protective measures align with global healthcare regulations. The model demonstrates high reliability (98%) and accuracy (90-95%) even with large data sizes (5-150 MB), ensuring scalability and robustness for real-world IoHT deployments.
Estimate Your IoHT Security ROI
Calculate the potential savings and efficiency gains by implementing secure, blockchain-enabled IoHT solutions in your healthcare organization.
Estimated Annual Cost Savings
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Your Secure IoHT Implementation Roadmap
A phased approach to integrating the Dual Attention-based Deep Bayesian Network with blockchain for robust multimedia data security in your organization.
Phase 1: Discovery & Infrastructure Assessment
Evaluate existing IoHT infrastructure, identify key multimedia data sources, and assess current security protocols. Define specific requirements and establish clear objectives for enhanced security and access control.
Phase 2: Blockchain Integration & DA-DBN Deployment
Implement the blockchain ledger for secure data storage and transaction traceability. Deploy the Dual Attention-based Deep Bayesian Network, configuring it to process user attributes and multimedia data for authentication and access control.
Phase 3: Customization & Training
Tailor the DA-DBN model to your organization's specific data types and regulatory needs (e.g., HIPAA, GDPR). Conduct comprehensive training for administrators and end-users on the new secure access control protocols.
Phase 4: Monitoring, Optimization & Compliance Audits
Establish real-time monitoring of the system for performance and security. Continuously optimize DA-DBN parameters and conduct regular compliance audits to ensure ongoing adherence to data privacy regulations and system effectiveness.
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