Data privacy protection in public health frameworks via legal and policy integration

The increasing reliance on data-driven methodologies in public health frameworks has led to significant advancements in disease surveillance, resource allocation, and policy-making. However, the integration of sensitive personal data into these frameworks raises critical concerns regarding data privacy and compliance with legal and ethical standards. Traditional approaches often fall short in effectively balancing data utility with privacy protection, as they typically lack comprehensive integration of legal and policy considerations.

This paper introduces a novel methodology, the Legal Privacy Dynamics Encoder, designed to incorporate legal and policy considerations into computational mechanisms, ensuring robust data privacy protection while maintaining data utility. The methodology is structured into three interconnected modules: the Constraint-driven Policy Mapper, the Agent-based Compliance Forecaster, and the Uncertainty-aware Risk Evaluator. These modules collectively address the challenges of translating legal and policy requirements into actionable constraints, forecasting compliance outcomes, and quantifying risks under uncertainty. The framework employs Privacy-Constrained Optimization with Probabilistic Compliance to enhance adaptability and robustness across diverse public health scenarios.

Experimental results demonstrate that the proposed methodology significantly improves compliance with legal standards while preserving data utility, achieving a balance that traditional methods have struggled to attain. By bridging the gap between technical data privacy mechanisms and legal frameworks, this work provides a comprehensive and enforceable solution to the challenges of data privacy protection in public health contexts, ultimately contributing to more effective and ethically sound public health data management.