An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural...

Area of Science:

• Cybersecurity
• Health Informatics
• Cryptography

Background:

• The Internet of Things (IoT) presents security challenges like data breaches and unauthorized access.
• Securing Patient Health Records (PHR) in the Internet of Medical Things (IoMT) is critical, with keyword search mechanisms being vulnerable.
• Existing blockchain frameworks for healthcare often use it merely as a database, leaving security gaps.

Purpose of the Study:

• To propose a novel, secure, and efficient mechanism for accessing Patient Health Records (PHR) in the Internet of Medical Things (IoMT).
• To address security vulnerabilities in keyword-based PHR access and enhance data privacy.
• To integrate blockchain technology with homomorphic encryption for a robust healthcare data access scheme.

Main Methods:

• A hybrid deep neural network approach combined with a Group Theory (GT)-based Binary Spring Search (BSS) algorithm for intrusion detection.
• Implementation of privacy-preserving technology using a blockchain-based methodology.
• Utilizing blockchain as a distributed database with homomorphic encryption for secure, keyword-based data access and a secure key revocation mechanism.

Main Results:

• The proposed approach effectively detects intrusions within IoT networks.
• A secure patient healthcare data access scheme is devised, integrating blockchain and trust chain.
• The framework demonstrates enhanced security, efficiency, and transparency compared to benchmark models.

Conclusions:

• The novel approach provides a more secure and efficient searchable mechanism for healthcare systems.
• Integrating blockchain with homomorphic encryption significantly improves the security of PHR access.
• The proposed scheme offers cost-effectiveness and transparency in sharing digital healthcare data.