Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Ganadily, Naif A., Xia, Han J.
Institution: University of Washington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4400023142