Enhancing Healthcare Data Privacy in Cloud IoT Networks Using Anomaly Detection and Optimization with Explainable AI (ExAI)

The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in cloud-based Internet of Things (IoT) networks, and it is optimized using explainable artificial intelligence. For anomaly detection, the Radial Boltzmann Gaussian Temporal Fuzzy Network (RBGTFN) is used in the process of doing information privacy analysis for healthcare data. Remora Colony Swarm Optimization is then used to carry out the optimization of the network. The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study. This evaluation suggested that the model measures the accuracy, precision, latency, Quality of Service (QoS), and scalability of the model. A remarkable 95% precision, 93% latency, 89% quality of service, 98% detection accuracy, and 96% scalability were obtained by the suggested model, as shown by the subsequent findings.