Privacy-Preserving Machine Learning Techniques in Electronic Health Records: Challenges and Solutions

Privacy-Preserving Machine Learning Techniques in Electronic Health Records: Challenges and Solutions

Author: Siddhi Anurag Sharma, Research Scholar, Faculty of Computer Application, University of Technology, Jaipur, Rajasthan, siddhisharma903@gmail.com

Co-Author: Dr. Suraj.V.Pot, Professor, Research Scholar, Faculty of Computer Application, University of Technology, Jaipur, Rajasthan

Abstract

The integration of machine learning (ML) techniques with electronic health records (EHRs) has shown immense potential in revolutionizing healthcare delivery, clinical decision-making, and medical research. However, this integration raises significant privacy concerns due to the sensitive nature of health data. This research paper explores the challenges and solutions associated with privacy-preserving machine learning techniques in the context of electronic health records. We investigate various approaches such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation, analyzing their effectiveness in maintaining data privacy while enabling valuable insights from EHRs. Our study employs a comprehensive literature review, case studies, and experimental simulations to evaluate these techniques. The results indicate that while each method offers unique advantages, a hybrid approach combining multiple privacy-preserving techniques may provide the most robust solution for protecting patient privacy in ML-driven EHR systems. This research contributes to the ongoing dialogue on balancing the benefits of ML in healthcare with the imperative of safeguarding patient confidentiality in the digital age.