SECURE FEDERATED LEARNING SYSTEM FOR HEALTHCARE DATA MANAGEMENT WITH PRIVACY PRESERVATION AND REGULATORY COMPLIANCE

Abstract

The invention introduces a system and method for secure healthcare data management using federated learning, advanced encryption, and compliance monitoring. The system enables healthcare institutions to train machine learning models locally on sensitive data without transferring raw information, ensuring privacy and regulatory compliance. Model updates are encrypted using robust cryptographic techniques, such as AES and RSA, and transmitted securely to a central aggregator. Privacy-preserving protocols, including secure aggregation and differential privacy, ensure confidentiality during the creation of a global model that integrates insights from multiple institutions. The global model is validated locally, ensuring contextual relevance and continuous improvement. The system also incorporates real-time compliance monitoring to automate adherence to standards like HIPAA and GDPR, with detailed logging and corrective actions. Modular architecture supports seamless integration with existing infrastructures and flexible deployment options. This invention offers a scalable, secure, and privacy-preserving framework tailored to the complex demands of healthcare data security.

CPC Classifications