Lightweight privacy preservation blockchain framework for healthcare applications using GM-SSO - Adla Padma; Mangayarkarasi Ramaiah (Results in Engineering)
Data security and privacy are crucial for the Internet of Medical Things (IoMT) and the digitization of healthcare systems. IoMT offers a revolutionary approach to healthcare monitoring, enabling remote patient interaction, sensor data collection, and even in-body implants. However, its wireless nature creates significant security vulnerabilities, and robust security mechanisms are essential to ensure patient trust. Hence, a lightweight privacy preservation mechanism based on blockchain and consensus is developed with minimal computational overhead. Initially, the reputation score is computed for every registered node using the confidence threshold value and transaction to verify the trustworthiness of the IoMT nodes. Tiny Feistel Cipher Encryption technique (TFCEA), a lightweight cryptographic technique, has been proposed to encrypt IoMT data. Based on the computed trust score, an Adaptive Proof of Work (APoW) consensus algorithm has been used to regulate block creation. APoW implements the Genetically Modified Salp Swarm Optimization (GM-SSO) algorithm for the node selection. To make it lightweight, the experimentation used the lightweight cryptographic algorithm and fine-tuned consensus APOW to facilitate the addition of new blocks according to their trust score. In terms of performance analysis, the presented experiments are benchmarked with the well-established works in the candidate domain. Tested results reveal its economic affordability by attaining 243.513 ms for 500 records as an execution time. An informal security analysis has been carried out to justify the robustness of the presented framework against various attacks. To increase resilience against cyber-attacks and ensure reliability in healthcare data security, Modified Principal Component Analysis (MPCA) enabled anomaly detection models were built using the BoT-IoT dataset. Tested models achieve 99.99% and 98.75% accuracy, indicating that the developed model is more suitable to prevent the possible cyber-attacks anticipated in smart healthcare.