The Privacy-Preserving High-Dimensional Synthetic Data Generation and Evaluation in the Healthcare Domain

Abstract

In the fast-changing environment of healthcare research and technology, there is an increasing demand for varied and vast information. However, issues with data privacy, unavailability, and ethical considerations frequently limit smooth access to true high-dimensional healthcare data. This research investigates a viable approach to addressing these challenges: the use of high-dimensional synthetic data in the healthcare area. The authors investigate the potentials and uses of synthetic data production through a review of current literature and methodology, providing insights into its role in overcoming data access barriers, fostering innovation, and supporting evidence-based decision making. The chapter outlines significant use cases, such as simulation and prediction research, hypothesis and algorithm testing, epidemiology, health information technology development, teaching and training, public dataset release, and data connecting.