用知识图谱实现灾害数据共享中的隐私合规，支持智能放行、禁止和变换后放行。
Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing
- 构建灾害管理与隐私政策双知识图谱，实现动态合规判断。
- 在510万三元组图上实现毫秒级决策，准确率达100%。
- 适合应急响应、数据共享平台等需严格隐私管控的场景。
灾害响应需要在重叠的隐私法规下共享异构数据，包括表格援助记录和无人机影像。现有系统常将合规简化为二元访问控制，在紧急流程中易失效。本文提出基于道义知识图谱的框架，融合灾害管理知识图谱（DKG）与源自IoT-Reg及FEMA/DHS的政策知识图谱（PKG）。释放决策函数支持三种结果：允许、阻止、允许-变换。后者绑定转换义务，并通过溯源关联的衍生数据验证转换后合规性；被阻止请求则记录为语义隐私事件。在含31.6万张图像的510万三元组DKG上评估显示，决策完全正确，单次决策延迟低于1秒，支持单图与联邦工作负载下的交互式查询性能。
原文摘要 · Abstract (English)
Disaster response requires sharing heterogeneous artifacts, from tabular assistance records to UAS imagery, under overlapping privacy mandates. Operational systems often reduce compliance to binary access control, which is brittle in time-critical workflows. We present a novel deontic knowledge graph-based framework that integrates a Disaster Management Knowledge Graph (DKG) with a Policy Knowledge Graph (PKG) derived from IoT-Reg and FEMA/DHS privacy drivers. Our release decision function supports three outcomes: Allow, Block, and Allow-with-Transform. The latter binds obligations to transforms and verifies post-transform compliance via provenance-linked derived artifacts; blocked requests are logged as semantic privacy incidents. Evaluation on a 5.1M-triple DKG with 316K images shows exact-match decision correctness, sub-second per-decision latency, and interactive query performance across both single-graph and federated workloads.
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