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

2024/06/23 by Naif A. Ganadily, Ganadily, Naif A., Han J. Xia +1
Computer Science · #Cryptography and Security (cs.CR) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2406.15962

openalex publication_date 2024/06/23 · openalex created_date 2024/06/26 · openalex updated_date 2026/07/28

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.

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