Privacy-Preserving Machine Learning for Healthcare Data Analysis

Thesis Abstract

Abstract

Healthcare data analysis is crucial for medical research and improving patient care, but it raises significant privacy concerns. This research focuses on the development and implementation of privacy-preserving machine learning techniques for healthcare data analysis. The study begins with a comprehensive review of healthcare data analysis, privacy challenges, privacy-preserving machine learning techniques, and existing approaches. A detailed methodology for privacy requirements analysis, selection of privacy-preserving machine learning algorithms, and protocol design is presented. The implementation phase involves the development of privacy-preserving machine learning models, integration of privacy-preserving protocols in healthcare data analysis, and performance evaluation. The results are analyzed, compared with conventional methods, and visualized to demonstrate the trade-offs between privacy and utility. The thesis concludes with a summary of research contributions, implications, and recommendations for future work in the field of privacy-preserving machine learning for healthcare data analysis. This research is expected to provide valuable insights and practical solutions for addressing privacy concerns in healthcare data analysis.