Dual layer security framework for privacy preserving AI driven healthcare edge cloud analytics

Ensuring the security and privacy of healthcare data in cloud ecosystems remains a critical challenge, especially with the increasing reliance on AI-driven predictive analytics. This study presents a Dual-Layer Privacy-Preserving AI Framework designed to enhance security, scalability, and real-time processing in healthcare applications. The proposed framework integrates adaptive encryption that dynamically adjusts security levels based on data sensitivity, ensuring robust protection without compromising computational efficiency. Additionally, it leverages privacy-preserving AI techniques, including federated learning and differential privacy, to maintain regulatory compliance while preserving analytical accuracy. A hybrid edge-cloud architecture enables real-time data processing at the edge, reducing latency while utilizing cloud resources for AI model training and secure storage. Furthermore, AI-driven proactive security mechanisms, such as real-time anomaly detection and predictive threat analysis, mitigate emerging cyber threats before they impact system integrity. Experimental evaluations demonstrate latency reductions to 30–50 ms, attack detection accuracy of 98%, and scalable throughput exceeding 10,000 data points per second, setting a new benchmark for secure, AI-powered healthcare analytics. This framework provides a transformative approach to balancing security, privacy, and efficiency in modern healthcare systems, paving the way for more trustworthy and resilient AI-driven medical solutions.