Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing

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2601.03587v1.pdf (940.5 KB)

Links to Files

http://arxiv.org/abs/2601.03587

Permanent Link

https://doi.org/10.48550/arXiv.2601.03587
 http://hdl.handle.net/11603/41749

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-6354-1686
 https://orcid.org/0009-0006-8930-2612

Date

2026-01-07

Type of Work

journal articles
preprints
Text

Department

Program

Citation of Original Publication

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Computer Science - Databases
UMBC Ebiquity Researh Group
UMBC Knowledge, Analytics, Cognitive and Cloud Computing (KnACC) lab
UMBC Cybersecurity Institute
Computer Science - Artificial Intelligence
Computer Science - Cryptography and Security

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.