Model Utility in Health Data Repositories under Privacy Budgets and Context Awareness

Abstract

The rapid digitization of medical records and the widespread deployment of health data repositories have created unprecedented opportunities for advancing medical research, public health surveillance, and personalized medicine. However, the utilization of these highly sensitive datasets introduces profound privacy concerns. Differential privacy has emerged as a gold standard for quantifying and bounding privacy risks, primarily governed by the allocation of a privacy budget. A critical challenge remains the inherent trade-off between the stringency of the privacy budget and the downstream utility of machine learning models trained on the sanitized data. This paper provides a comprehensive academic analysis of how model utility can be systematically explained, preserved, and optimized by integrating context awareness into the privacy budget allocation process. We propose a theoretical framework wherein privacy parameters are dynamically adjusted based on the contextual sensitivity of the data, the specific characteristics of the medical inquiry, and the anticipated operational environment of the predictive models. Through extensive discussion of simulation designs and resulting utility metrics, we demonstrate that context-aware privacy budget management mitigates the severe performance degradation typically associated with strict, uniform privacy guarantees. The findings suggest that adopting domain-specific, contextually intelligent data sanitization strategies significantly enhances predictive accuracy without compromising the fundamental privacy rights of individuals represented in health repositories.