Efficient Security and Authentication for Edge-Based Internet of Medical Things.

Area of Science:

• Computer Science
• Information Security
• Healthcare Technology

Background:

• The Internet of Medical Things (IoMT) is transforming healthcare with advanced technologies for data collection and automation.
• Secure and authenticated exchange of electronic health records (EHRs) is critical for patient data privacy and integrity.
• Existing methods for EHR security may lack sufficient payload capacity, imperceptibility, or tamper detection capabilities.

Purpose of the Study:

• To develop a novel, high-payload, and reversible EHR embedding framework for enhanced security and authentication in IoMT.
• To improve the protection of sensitive patient information transmitted within smart health systems.
• To provide a robust solution for tamper detection and localization of embedded EHR data.

Main Methods:

• The proposed framework utilizes Left Data Mapping (LDM), Pixel Repetition Method (PRM), RC4 encryption, and checksum computation.
• PRM is employed for image upscaling to ensure reversibility with reduced computational complexity.
• Encrypted EHR data is embedded into cover images using LDM encoding and block-based pixel manipulation, with checksums for tamper detection.

Main Results:

• The framework achieves a high embedding capacity of 2.25 bits per pixel with an average PSNR of 41.95 dB, indicating good imperceptibility.
• Embedding time is notably fast, less than 0.2 seconds.
• Experimental results demonstrate superior performance over state-of-the-art techniques in payload, imperceptibility, computational efficiency, and tamper detection.

Conclusions:

• The developed EHR embedding framework offers a promising solution for secure data exchange and authentication in IoMT-based smart health.
• The approach effectively balances high payload capacity, data integrity, and computational efficiency.
• The proposed method enhances the security and reliability of healthcare data management systems.