Introduction 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. Methods 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 int
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