Bridging the gap through a literature review on enhancing anonymization techniques for seamless integration within the IoMT ecosystem.

Abstract

The Internet of Medical Things refers to a revolutionary change that interconnects medical devices and systems, enhancing the care being delivered to patients and improves the operational efficiency of these medical systems. However, integrating IoMT also opens some major challenges regarding data security and privacy. Therefore, a robust anonymization method must be developed to shield sensitive health information. This literature survey reviews the existing anonymisation techniques: k-anonymity, differential privacy, and data perturbation, which include their strengths and weaknesses to IoMT. Some significant challenges include scalability, as well as the real-time analysis of the data, whereas the standardization of such methodologies is required. There are even more advanced techniques like Homomorphic Encryption as well as Federated Learning that will help in upgradation in privacy protection. Recommendations for future research suggest hybrid approaches, interdisciplinary collaboration, and establishment of frameworks that will facilitate the effective implementation of anonymization techniques across diverse IoMT applications. Addressing these critical areas, this paper will contribute to the ongoing discourse on enhancing data privacy and security in the rapidly evolving landscape of IoMT.