A Novel Approach for Health Care Data Security employing Deep Learning Algorithms Compatible with HIPAA

Abstract

This paper presents a novel framework for securing healthcare data using deep learning techniques while maintaining compliance with the Health Insurance Portability and Accountability Act (HIPAA). The proposed approach combines advanced encryption methodologies with neural network-based anomaly detection to protect sensitive patient information. We introduce a hierarchical security model that employs autoencoders for data compression and reconstruction, adversarial networks for threat detection, and federated learning for privacy-preserving model training. Experimental results demonstrate that our approach achieves 99.3% accuracy in detecting unauthorized access attempts while maintaining system performance. The framework successfully addresses the unique challenges of healthcare environments by providing robust security measures without compromising data accessibility for authorized personnel. This research contributes to the growing field of AI-enhanced cybersecurity specifically tailored for healthcare institutions handling protected health information (PHI).