# Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing

arXiv: https://arxiv.org/abs/2601.03587 · HTML: https://mlsanity.com/paper/2601.03587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [A Survey on Security and Privacy Issues of UAVs](https://mlsanity.com/paper/2109.14442/) (2021)
- [Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence](https://mlsanity.com/paper/2508.16669/) (2025)

## Abstract (arXiv)

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.