Privacy-Preserving Machine Learning for Healthcare Data Analysis

Project Abstract

<p> <br>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. <br></p>

Project Overview