Secure GAN-Driven Cloud Intelligence for Big Data Healthcare Analytics with Randomized Interference Evaluation

Abstract

The growing adoption of cloud-based healthcare platforms and large-scale medical data analytics has introduced new challenges related to security, robustness, and reliability of artificial intelligence models. This paper presents a secure GAN-driven cloud intelligence framework designed for big data healthcare analytics with a focus on randomized interference evaluation. The proposed approach leverages Generative Adversarial Networks (GANs) to model complex data distributions, enhance anomaly detection, and generate synthetic healthcare data for robust testing and validation. Cloud-native security mechanisms, including encryption, access control, and isolation policies, are integrated to protect sensitive patient information and ensure regulatory compliance. Randomized interference evaluation is employed to assess model resilience under data perturbations, adversarial noise, and workload variability, enabling systematic analysis of stability and performance degradation. Experimental results demonstrate improved robustness, scalability, and fault tolerance of healthcare analytics pipelines under diverse interference scenarios. The framework provides a reliable foundation for secure, scalable, and trustworthy AI-driven healthcare analytics in cloud environments.