Tp-DyHEQN: Trust Priority based Dynamic Homomorphic Elliptic Curve Encryption Algorithm Enabled Blockchain based Deep Learning Model for Secure Data Sharing

Abstract

In recent times, data transmission in Wireless Sensor Network environments has become more prevalent. Nevertheless, these networks encounter numerous challenges during data transmission, including decreased network longevity, lower security, and lower energy efficiency. Furthermore, while authentication techniques ensure data authenticity, current mechanisms have security flaws such as identity spoofing, lack of transparency and scalability issues. Therefore, to overcome these challanges, a blockchain-based trust model known as trust priority-based dynamic homomorphic elliptic curve encryption algorithm enabled quantum convolutional neural network (Tp-DyHEQN), which enables secure data sharing and energy-efficient routing. The proposed trust priority-based energy loss minimization algorithm (Tp-EMA) enables energy-efficient routing, which minimizes the energy consumption of nodes and enhances the lifetime of the network. Additionally, the Tp-DyHEQN model detects the malicious activities of nodes, thus maintaining data privacy and safeguarding the network from unauthorized node activities. Moreover, the dynamic homomorphic elliptic curve encryption approach allows for computations to be performed on encrypted data without needing to decrypt it first enhancing network scalability and data integrity. The validation results prove that, compared to conventional approaches the proposed algorithm exhibits superior attack detection performance with an accuracy of 96.18% and a maximum privacy ratio of 0.92 for 500 nodes.