用AI提前检测医疗数据聚合查询的隐私风险，防患于未然。
Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance
- 通过解析SQL语句的语法结构和语义特征，识别潜在泄露风险
- 风险评分超0.85的查询会被标记并生成可读解释
- 适合医疗数据治理、合规审计与零信任架构场景
大型医疗机构通常按临床、筹资、运营和合规等业务领域划分多个数据分析团队。受HIPAA、FERPA及IRB限制，这些团队难以共享需患者级数据的分析资源。为此，提出一种度量聚合表，即预计算的隐私合规摘要，可在不直接访问敏感数据的前提下支持决策。然而，即使聚合指标也可能因设计不当导致隐私泄露。本文提出一个模块化AI框架，通过结合语义与语法特征评估基于SQL的指标定义是否存在过度暴露风险。系统将SQL查询解析为抽象语法树（AST），提取敏感模式（如按邮编或性别进行细粒度分组），利用预训练CodeBERT嵌入编码逻辑，并融合结构特征输入XGBoost分类器，生成风险评分。评分超过阈值（如>0.85）的查询将被标记并返回可读解释，实现部署前的主动治理。该方法无需运行时数据访问，可在查询阶段完成静态、可解释的风险检测，提升审计准备度，支持基于角色的访问控制（RBAC）和零信任数据架构，符合国家数据现代化目标，确保指标管道具备可解释性、隐私保护性和可审计性。
原文摘要 · Abstract (English)
Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundraising, operations, and compliance. Due to HIPAA, FERPA, and IRB restrictions, these teams face challenges in sharing patient-level data needed for analytics. To mitigate this, A metric aggregation table is proposed, which is a precomputed, privacy-compliant summary. These abstractions enable decision-making without direct access to sensitive data. However, even aggregated metrics can inadvertently lead to privacy risks if constructed without rigorous safeguards. A modular AI framework is proposed that evaluates SQL-based metric definitions for potential overexposure using both semantic and syntactic features. Specifically, the system parses SQL queries into abstract syntax trees (ASTs), extracts sensitive patterns (e.g., fine-grained GROUP BY on ZIP code or gender), and encodes the logic using pretrained CodeBERT embeddings. These are fused with structural features and passed to an XGBoost classifier trained to assign risk scores. Queries that surpass the risk threshold (e.g., > 0.85) are flagged and returned with human-readable explanations. This enables proactive governance, preventing statistical disclosure before deployment. This implementation demonstrates strong potential for cross-departmental metric sharing in healthcare while maintaining compliance and auditability. The system also promotes role-based access control (RBAC), supports zero-trust data architectures, and aligns with national data modernization goals by ensuring that metric pipelines are explainable, privacy-preserving, and AI-auditable by design. Unlike prior works that rely on runtime data access to flag privacy violations, the proposed framework performs static, explainable detection at the query-level, enabling pre-execution protection and audit readiness
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