Enhancing healthcare security in the digital era: Safeguarding medical images with lightweight cryptographic techniques in IoT healthcare applications

The Internet of Things has allowed for the connectivity of medical imaging devices to the healthcare sector's data backbone. This development, made possible by the IoT, will speed up the diagnosis and treatment phases of medical care. The increasing reliance on interconnected devices and cloud-based systems creates potential entry points for cyber-attacks and unauthorized access to sensitive medical data which not only jeopardize patient privacy but also have serious implications for patient safety and trust in healthcare systems. Medical images are critical assets in healthcare, and their confidentiality, integrity, and availability are paramount for accurate diagnoses, treatment planning, and patient care. This instigated research related to medical image security in IoT healthcare. For this, we investigated the usage of a cryptography-based network for image encryption and decryption, and its potential application to the safe transmission of medical images through deep learning. In the proposed work, we use a key learning network based on the ResNet-50 architecture to do the mapping between various image representations. Due to the incorporation of these hidden properties into the learning model, the encryption technique can be fine-tuned for each specific domain. As a first step in decryption, we use recon-structive networks to convert the encrypted image back into its plaintext form. Once the hidden entities have been uncovered, a Return on Investment (ROI) framework can be made available, and data mining can be simplified by drawing directly from the user's local information environment. Imaging tools for gauging therapy is a highly reliable by using the proposed system. We used two different kinds of publicly available datasets, and that helped us succeed in our mission. The comprehensive empirical setup and the findings of the security analysis suggest that the suggested method may give an unprecedented level of security and an unprecedentedly powerful outcome.

Automated summary

The Internet of Things enables the connectivity of medical imaging devices to healthcare data, speeding up diagnosis and treatment. However, it also brings risks of cyber-attacks and unauthorized access. Research on cryptographic network for image encryption and decryption in the IoT healthcare context is explored, with the potential application of deep learning for secure medical image transmission.