Deep Learning Methodology for Data Security in Healthcare

Abstract

The rapid digitalization of healthcare has resulted in a massive influx of sensitive patient data, making data security a critical concern. Traditional security methods often fall short in addressing the evolving complexity of cyber threats. Deep learning, a subset of artificial intelligence, offers advanced methodologies for enhancing data protection by leveraging neural networks to detect anomalies, predict vulnerabilities, and automate security protocols. This paper explores deep learning-based approaches for securing healthcare data, focusing on techniques such as intrusion detection systems (IDS), data encryption, privacy-preserving machine learning, and biometric authentication. The integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and auto encoders has demonstrated significant potential in identifying security breaches, safeguarding electronic health records (EHRs), and ensuring compliance with regulatory frameworks like HIPAA. Furthermore, deep learning facilitates real-time threat detection, adaptive defence mechanisms, and robust encryption methods, thereby strengthening data integrity, confidentiality, and availability. This study emphasizes the transformative role of deep learning in creating intelligent, scalable, and resilient security frameworks for healthcare, ultimately improving patient trust and the overall quality of care delivery.