Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
This addresses privacy concerns for healthcare providers and patients, but it appears incremental as it combines existing techniques without novel breakthroughs.
The paper tackles the problem of applying machine learning to electronic health records while preserving patient privacy by using federated learning and differential privacy, but it does not report specific results or numbers.
An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagnoses, treatments, costs, and other personal information. Machine learning (ML) algorithms can be used to extract and analyze patient data to improve patient care. Patient records contain highly sensitive information, such as social security numbers (SSNs) and residential addresses, which introduces a need to apply privacy-preserving techniques for these ML models using federated learning and differential privacy.