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This paper introduces PriEval-Protect, a two-phase framework designed to unify privacy risk evaluation and protection in healthcare systems, addressing the limitations of existing manual compliance methods. The framework integrates regulatory compliance scoring through a fine-tuned legal LLM with technical assessments of data architecture and encryption, culminating in a composite risk score generated via Analytic Hierarchy Process. Results indicate that PriEval-Protect provides regulation-aligned, explainable assessments that effectively bridge the gap between legal compliance and data-level risk analysis in healthcare contexts.
A unified framework that seamlessly integrates legal compliance and technical risk assessment could revolutionize how healthcare systems safeguard patient privacy.
Safeguarding patient privacy while enabling meaningful healthcare data use remains critical under GDPR and HIPAA. Existing compliance methods are manual, error-prone, and separate policy audits from data-level assessments. This paper presents PriEval-Protect, a two-phase framework for unified privacy risk evaluation and mitigation. The evaluation phase combines regulatory compliance scoring using a fine-tuned legal LLM with RAG, and technical analysis via encryption type, data architecture, and metrics including similarity, uncertainty, adversary success, and information gain/loss. A composite risk score uses weighted aggregation via Analytic Hierarchy Process. The protection phase recommends countermeasures including federated learning and differential privacy based on assessed risk. Results on hospital documents and datasets demonstrate regulation-aligned, explainable assessments, bridging legal conformance and data-level risk analysis.