Privacy Preservation for the IoMT Using Federated Learning and Blockchain Technologies

Within the dynamic landscape of smart healthcare, notable progress in the Internet of Medical Things (IoMT) technology, characterized by wearable sensors and vital sign monitors, delivers real-time health insights when linked to the Internet. However, the abundance of sensitive data transmitted by IoMT applications exposes them to security vulnerabilities, prompting critical privacy concerns. This paper meticulously explores privacy threats within the IoMT, discussing security requirements for enhancing privacy across its layers. By Investigating the established approaches addressing security, privacy, confidentiality, and integrity, the study delves into integrating Federated Learning (FL) with blockchain technology as an innovative solution to bolster security and privacy measures in IoMT devices within the healthcare sector. Beyond reviewing FL and blockchain, the paper serves as a valuable resource for researchers, providing insights into these technologies and addressing intricate security challenges within IoMT. By guiding researchers toward future directions in privacy preservation, the work contributes to advancing secure and privacy-conscious healthcare technologies, fostering a deeper understanding of IoMT intricacies within a concise framework.