Adaptive Privacy and Lightweight Security in Healthcare IoT: Challenges, Solutions and Future Directions

IoT in healthcare has transformed patient care by enabling and implementing the real-time monitoring for the end patients or users. However, the development of connected devices introduces significant privacy and security challenges, especially while securing the healthcare data. In this paper, a comparative review of adaptive privacy and lightweight security mechanisms in the area of healthcare IoT is shown. A comparison of current methods based on criteria including data security, privacy preservation, scalability, resource efficiency, and real-time implementation is one of the major contributions. Furthermore, this review recognizes the research gaps in the recent researches and highlights promising techniques, including advanced encryption, federated learning, and the edge computing. By addressing these challenges, the study aims to guide the development of robust, efficient, and scalable solutions that ensure privacy and security in dynamic healthcare IoT environments.