Design of an Iterative Method for Secure and Private IoT Healthcare Data Management Using Encrypted Federated Learning and AI-Driven Anomaly Detection

Abstract

The escalating need for robust, privacy-preserving IoT Healthcare data management systems has prompted the exploration of security models that ensure non-mutability and stringent security. Traditional methods often fall short in effectively balancing privacy, accessibility, and computational efficiency. Addressing these limitations, this paper introduces a novel framework utilizing encrypted federated learning, access control, anomaly detection, and predictive analytics tailored for IoT Healthcare applications. Our proposed model comprises four innovative methods: Encrypted Federated Learning with Homomorphic Encryption (EFLHE), Reinforcement Learning-Driven Access Control (RLAC), AI-Driven Anomaly Detection with Autoencoder Fusion (AIDA), and timestamp Series-Based IoT Healthcare Forecasting with Grey Wolf Optimizer (TS-GWO). EFLHE harnesses homomorphic encryption to train machine learning models on encrypted data across multiple nodes, preserving patient confidentiality while enabling decentralized computation. This method overcomes the existing challenges of data privacy and computational overhead associated with traditional federated learning systems. Furthermore, RLAC employs reinforcement learning to dynamically optimize access control policies via smart contracts based on real-time interaction and system feedback, thus enhancing both security and user experience. This adaptive control mechanism significantly outperforms static access control systems in responding to evolving security threats and user requirements. In parallel, AIDA integrates autoencoders with AI-driven models to meticulously detect anomalies and potentially fraudulent activities within the network. By learning standard transaction patterns and identifying deviations, AIDA provides a dual-layer security framework that significantly reduces the risk of security breaches. Lastly, TS-GWO leverages the Grey Wolf Optimizer to refine the parameters of timestamp series forecasting models. This optimization facilitates more accurate predictions regarding disease progression, treatment outcomes, and resource allocation, which are critical for proactive IoT Healthcare management. Collectively, these methods not only fortify the security and privacy of IoT Healthcare data but also enhance the operational efficiency of IoT Healthcare systems. The impacts of this work are profound, offering a scalable, secure, and efficient framework for IoT Healthcare data management that meets the rigorous demands of modern IoT Healthcare infrastructures and compliance standards. This model sets a new benchmark for privacy-preserving, real-time IoT Healthcare data systems, potentially revolutionizing patient care through technologically advanced solutions that safeguard sensitive information and optimize clinical decision-making processes.