A Novel Privacy-Preserved Iot Data Protection Model Using APO for Optimal Key Generation
B. Priyanka · Journal of Networking and Communication Systems (JNACS) · 2024
The Internet of Things (IoT) has paved the way for a highly interconnected world, where devices exchange information seamlessly, enhancing the quality of human life across various applications.As the number of connected devices continues to rise, concerns about sensitive data disclosure have emerged as a critical issue.Users' privacy on IoT devices is particularly concerning due to the threats posed by malware and hackers, especially given the rapid expansion of IoT in commercial and critical infrastructure settings.To address these challenges, this research introduces an effective approach for a privacy-preserved IoT data protection model called Artificial Protozoa Optimization (APO) for optimal key generation.The data owner uploads information to the cloud, converting it to polynomial values for privacy.RV coefficients are identified to assess relationships, and bilinear transformation anonymizes data.An optimal key is generated and encrypted, allowing users to access the original data.This approach aims to enhance the security and privacy of IoT data, making it a valuable contribution to the field.In this process, the data owner first uploads all information to a cloud model.The established APO method has demonstrated impressive results, achieving an overall conditional privacy rate of 93.456% and a normalized variance of 0.921.