Healthcare Meets AI: Enhancing Big Data Security with Graph-Based Learning

Abstract

In the era of digital transformation, the healthcare sector faces growing concerns regarding the security and privacy of sensitive patient data. With the proliferation of big data in healthcare, there is an increasing need to employ advanced AI techniques to enhance data security. This paper presents a novel approach to improving healthcare data protection by integrating graph-based learning models within AI-driven frameworks. Graph-based learning techniques, such as Graph Neural Networks (GNNs), are employed to model and analyze complex relationships within healthcare data, detecting potential threats, anomalies, and security breaches. The proposed approach leverages the structured nature of healthcare data, including Electronic Health Records (EHRs), genomic data, and patient interaction networks, to build secure models that can identify vulnerabilities and respond dynamically to security threats. Through extensive experimentation and performance analysis, we demonstrate that graph-based learning models can provide higher accuracy, scalability, and adaptability in identifying cyber threats compared to traditional methods. Moreover, the integration of Federated Learning with graph-based models ensures that patient privacy is preserved by allowing data processing across decentralized systems without the need to share sensitive data. This paper offers a comprehensive overview of the potential of AI-driven graph-based learning models in securing healthcare big data and outlines key challenges and future directions in the field.