Hybrid Adaptive Renyi-Exponential Differential Privacy for Internet of Things (IoT) Network Security in Dynamic Environment

The proliferation of Internet of Things (IoT) devices has led to an abundance of sensitive data being transmitted and processed in IoT networks. Ensuring privacy in such networks is crucial to protect user information from unauthorized access and misuse. In this paper, we propose HAREDP (Hybrid Adaptive Renyi-Exponential Differential Privacy), a novel approach that combines Adaptive Renyi Differential Privacy and Adaptive Exponential Differential Privacy to preserve privacy in IoT network security. By integrating the strengths of both techniques, HAREDP offers a robust and adaptable solution for privacy preservation in dynamic IoT environments. Integrating Adaptive Renyi Differential Privacy and Adaptive Exponential Differential Privacy, HAREDP offers a comprehensive solution for privacy preservation in IoT network security. The adaptive privacy mechanisms of both techniques enable effective privacy protection in dynamic IoT environments, ensuring the confidentiality of sensitive data. Experimental evaluation and a real-world case study validate the effectiveness of HAREDP in preserving privacy in IoT networks. The accuracy of the analysis is 98.78% indicating the proportion of correctly classified instances. The precision of the analysis is 98.78%, representing the proportion of true positive instances among the predicted positive instances. The sensitivity is 98.78%, represents the proportion of actual positive instances correctly identified. The privacy utility achieved by HAREDP is 0.993844128, this measures the usefulness of the analysis results while preserving privacy. The privacy trade-off ratio is 0.050833194, indicating the ratio between privacy loss and privacy utility. A higher value signifies a greater trade-off between privacy and utility.