Balancing Privacy and Utility: Evaluating Disclosure Risk and Utility Metrics in Synthetic Health Data

Thomas M. Menino Convention & Exhibition Center 

As synthetic data generation becomes an increasingly viable approach for privacy-preserving microdata dissemination, evaluating the trade-offs between privacy protection and analytical utility remains a key challenge. This presentation focuses on the disclosure risk and utility metrics developed for synthetic electronic health record (EHR) data, designed to be both statistically faithful and privacy-safe. Treating a publicly available synthetic EHR dataset as original data, we generate synthetic health data and assess disclosure risk and data utility across a suite of key metrics, such as distributional similarity, correlation preservation, and model fidelity, to quantify these trade-offs. We also demonstrate how visualizing the balance between disclosure risk and data utility provides an intuitive framework for communicating privacy–utility trade-offs to both technical and applied audiences. This work highlights practical lessons learned from developing and evaluating synthetic data at scale, contributing to emerging best practices that align robust privacy protections with actionable data utility.