A Hybrid Optimization Approach to Enhance Source Location Privacy for IoT Healthcare

Abstract

The privacy protection of the transmitted data is considered a vital issue encountered by most of the Internet of Things (IoT) platforms. In recent years, the privacy of users has been protected by employing many location privacy-protection algorithms. These methods often use static phantom node selection that lacks adaptability. This paper proposed an innovative Fractional Flamingo Archery Optimization (FFAO) to select the optimal phantom node and identify a suitable routing path for source location privacy in an IoT-enabled blockchain healthcare network. The hybrid approach is based on multi-objective optimization parameters, such as distance, energy, neighbor list, trust, and heterogeneity, to enhance adaptability and security. In addition, blockchain is integrated with FFAO to add an extra layer of protection. The performance of FFAO during optimal routing path selection is validated through simulation in MATLAB environment, and it attained a maximum safety period of 563501.7m, network lifetime of 119.679s, and energy of 0.125J. These findings highlight the effectiveness, scalability, and improved location privacy compared to existing techniques for real-world IoT healthcare applications.

Keywords

• Blockchain for privacy
• phantom routing
• search optimization
• source location privacy
• Internet of Things
• IoT healthcare security