Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy

-- 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. With the increased application of machine learning (ML) in the healthcare industry to diagnose patients or to prescribe The patient service scheme shown in Figure 1 has three medication, applying a privacy-preserving machine learning primary functions: (1) to provide a means of communication (PPML) framework for Electronic Health Record (EHR) with patients, (2) to control the flow of information, and (3) to systems allows healthcare providers to collaboratively train allow verifiers to ensure that all agreed-upon policies have and evaluate ML models without exposing sensitive patient been enforced. The policy management allows patients to records.