Developing Privacy-Preserving Machine Learning Models for Sensitive Data

Abstract

This study explores the development of privacy-protecting machine learning models that balance data utility and privacy, especially in sensitive areas such as healthcare. By generating synthetic datasets with various privacy techniques, the study assesses how these methods affect data fidelity. Through statistical modeling and comparative analysis using metrics such as Wasserstein-1 distance and related heat maps, the results show that the optimized synthetic data preserves critical analytical value while protecting personal information. The study concluded that a balance between utility and privacy can be achieved with appropriate privacy budgets and methodological adjustments.