Security and privacy challenges in healthcare IoT systems with cloud, edge, and fog computing: A systematic literature review

Purpose: The integration of the Internet of Things (IoT) in healthcare with cloud, edge, and fog computing offers significant benefits to healthcare, including real time data processing, enhanced patient care, and cost reductions through remote monitoring. However, these advancements present substantial challenges regarding data security and privacy, particularly in areas such as access control, AI-driven analytics, and compliance with privacy regulations. To address these concerns, robust security measures like encryption, multi-factor authentication, and effective access control mechanisms are necessary. This Systematic Literature Review (SLR) aims to explore existing methods, research contributions, limitations, and future research directions to improve security and privacy in IoT systems within healthcare across cloud, fog, and edge computing. Methods: The review analysed seventy-two peer-reviewed studies that were published between 2015 and October 2025, identifying key categories of methods used to enhance the security of healthcare IoT systems. These studies were evaluated based on their use of cryptographic techniques, authentication mechanisms, machine learning approaches, privacy preserving methods, architecture designs, and evaluation frameworks. The SLR focuses on security methods tailored for healthcare IoT’s unique resource constraints and diverse computing environments, examining how these strategies mitigate data security risks. Results: The analysis revealed six primary categories of security enhancement methods. Cryptography, used in 32 studies, plays a vital role, with techniques such as Elliptic Curve Cryptography and Quantum Cryptography improving data protection in healthcare IoT environments. Authentication methods, particularly multi-factor and lightweight approaches, appeared in 23 studies to prevent unauthorized access. Artificial Intelligence methods (19 studies), including machine learning, deep learning, and federated learning approaches, enable real-time threat detection and advanced analytics. Privacy-preserving techniques (13 studies), such as Fully Homomorphic Encryption, support secure data sharing, while architecture design (39 studies), especially edge and fog computing, enhances system scalability and security. Recent studies confirm this trend (2024-2025), with a notable surge in 2025 publications focusing on hybrid fog-edge architectures achieving high latency reduction, AI-driven anomaly detection reaching very high accuracy, explainable AI for regulatory compliance, 5G-enhanced authentication with sub-millisecond overhead, and privacy-preserving frameworks maintaining very high accuracy. Conclusion: While significant progress has been made, gaps remain, particularly in lightweight encryption, hardware-agnostic solutions, and real-world testing. Future research must address these issues, focusing on optimising edge and fog computing strategies, exploring blockchain for decentralised security, and balancing usability with robust protection to ensure secure, scalable healthcare IoT systems.