Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing
Kelvin Uzoma Echenim, Karuna Pande Joshi

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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Privacy-Preserving Technologies in Data · Opportunistic and Delay-Tolerant Networks
