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Adaptive deep conditional random field with blockchain for secure data sharing in software-defined wireless body area

Sayila Subrahmanyam¹, Rajesh Arunachalam², Surendra Kumar Shukla³

¹Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, 602105, Chennai, Tamil Nadu, India.

Scientific Reports

|May 25, 2026

Summary

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This summary is machine-generated.

A new secure data-sharing framework for Software-Defined Wireless Body Area Networks (SDWBANs) uses blockchain and advanced encryption. This model achieves high accuracy (98.7%) for sensitive health data protection.

Area of Science:

Cybersecurity in Healthcare
Wireless Body Area Networks
Blockchain Technology

Background:

Sharing sensitive patient data in healthcare faces integrity and confidentiality challenges.
Existing models struggle to ensure privacy and controlled access in Software-Defined Wireless Body Area Networks (SDWBANs).

Purpose of the Study:

To design a secure data-sharing framework for SDWBANs addressing privacy and access control for sensitive medical information.
To enhance data integrity and confidentiality in healthcare data transmission.

Main Methods:

Integration of blockchain technology and encryption techniques for secure data handling.
Utilizing Adaptive Deep Conditional Random Field (ADCRF) for decision-making.
Employing Optimal Key-based Multi-Authority Attribute-Based Encryption (O-MA-ABE) for data access control.

Keywords:

Adaptive Deep Conditional Random Field Enhanced Piranha Foraging Optimization Algorithm Optimal Key-based Multi-Authority Attribute-Based Encryption Software Defined Wireless Body Area Network

Related Experiment Videos

Optimizing ADCRF hyperparameters and O-MA-ABE keys using the Modified Escape Search-based Piranha Foraging Optimization Algorithm (MES-PFOA).

Main Results:

The MES-PFOA-O-MA-ABE + ADCRF model achieved 98.7% accuracy and 98.3% precision.
Demonstrated minimal encryption time (210 ms) and high throughput (275 TPS).
Outperformed existing data-sharing models like DTAC-TL-QM, SCCE-DS, BFL-hIoT, and PPFL-ICP.

Conclusions:

The developed framework effectively secures sensitive health data in SDWBANs.
The combination of MES-PFOA, O-MA-ABE, and ADCRF provides a robust solution for secure and efficient healthcare data sharing.
Achieved superior performance in accuracy, precision, encryption time, and throughput compared to contemporary methods.