Advancing IoT Healthcare Security: A Hamiltonian Quantum Generative Adversarial Network Approach with Enhanced Privacy Protection

Abstract

The Internet of Things (IoT) is transforming healthcare by enabling real-time communication between doctors, patients, medical devices, and healthcare systems for improved monitoring and treatment. However, the complexity and scale of IoT networks pose significant challenges in ensuring the privacy and security of sensitive medical data. Due to the energy limitations of network devices, modern healthcare systems require security solutions that are both efficient and robust. To address these issues, this paper presents an Optimized Hamiltonian Quantum Generative Adversarial Network (HQ-GAN) integrated with Safe Data Transmission Techniques for privacy preservation in IoT healthcare applications. The model enhances data protection during transmission, safeguarding it from potential threats while reducing the time needed for encryption and decryption. The core of the system involves Hamiltonian Quantum GANs, optimized using the Zebra Optimization Algorithm to improve data scheduling and transmission efficiency, thereby reducing network traffic and ensuring timely medical record delivery. Additionally, the framework employs CKKS homomorphic encryption to ensure secure data sharing. This technique allows encrypted computations without decryption, preserving patient and hospital data privacy. Overall, this model offers a secure and efficient solution for IoT-based healthcare systems by enhancing quantum network performance while reducing computational and communication overhead