Between Privacy and Utility: Navigating Inference Risks in De-Identified Health Data

Abstract

Protecting healthcare data from inference attacks, where adversaries deduce sensitive information from de-identified data, is critical. This study examines the vulnerability of such datasets, focusing on Tennessee facilities serving predominantly African American populations, while also incorporating analyses based on the MIMIC-III dataset representing Massachusetts. We apply differential privacy with varying ϵ values to assess its impact on statistical integrity and predictive model accuracy. Results show a clear trade-off: lower ϵ enhances privacy but degrades performance, while higher ϵ preserves utility at the cost of increased leakage risk. These findings underscore the importance of carefully balancing privacy and utility when allocating the privacy budget in clinical prediction tasks.

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