Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing

TL;DR

This paper introduces a deontic knowledge graph framework for privacy-compliant disaster data sharing, enabling nuanced access decisions beyond binary controls, with efficient real-time performance.

Contribution

It presents a novel integration of disaster management and privacy policy knowledge graphs using deontic logic for dynamic, compliant data sharing decisions.

Findings

Exact-match decision correctness achieved

Sub-second decision latency demonstrated

Effective performance in federated workloads

Abstract

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.