An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives

Computer Science > Cryptography and Security [Submitted on 15 Apr 2026] An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives Junyi Ma, Pei Li, Rui Gan, Kai Cheng, Steven T. Parker, Bin Ran Crash narratives in crash reports provide crucial contextual information for traffic safety analysis. Yet, their broader use is hindered by the presence of personally identifiable information (PII), including names, home addresses, and license plate numbers. Because PII appears sparsely and inconsistently in crash narratives, manual detection is not scalable, and existing rule-based approaches often fail to capture context-dependent PII. This study develops and evaluates a locally deployable, agentic workflow for PII detection in crash narratives by leveraging large language models (LLMs). The workflow contains a Hybrid Extractor and a Verifier. The Hybrid Extractor routes structured PII (e.g., phone numbers and email addresses) to a rule-based model (i.e., Presidio) and context-dependent PII (e.g., names, home addresses, and alphanumeric identifiers) to a domain-adapted, fine-tuned LLM. To address ambiguity in challenging categories, the workflow incorporates ensemble LLM extraction and an agentic verification step that filters false detections through evidence-based reasoning. Evaluated on a real-world crash dataset, the agentic workflow achieves strong performance with a precision of 0.82, a recall of 0.94, an F1 of 0.87, and an accuracy of 0.96, outperforming multiple baseline methods. Moreover, the ablation results suggest that ensemble LLM extraction and Verifier offer improved detection for home addresses and alphanumeric identifiers. The workflow runs locally, supporting privacy-sensitive operational settings where external APIs are restricted. This work offers a practical and robust path for scalable, privacy-preserving crash data processing, enabling broader research and safety interventions while safeguarding individual privacy. Comments: 13 pages, 5 figures Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.15369 [cs.CR]   (or arXiv:2604.15369v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.15369 Focus to learn more Submission history From: Junyi Ma [view email] [v1] Wed, 15 Apr 2026 05:03:20 UTC (632 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)