This work presents AnonLFI 2.0, a modular pseudonymization framework for CSIRTs that uses HMAC SHA256 to generate strong and reversible pseudonyms, preserves XML and JSON structures, and integrates OCR and technical recognizers for PII and security artifacts. In two case studies involving OCR applied to PDF documents and an OpenVAS XML report, the system achieved perfect precision and F1 scores of 76.5 and 92.13, demonstrating its effectiveness for securely preparing complex cybersecurity datasets.
Cite
@article{arxiv.2511.15744,
title = {AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizers},
author = {Cristhian Kapelinski and Douglas Lautert and Beatriz Machado and Diego Kreutz},
journal= {arXiv preprint arXiv:2511.15744},
year = {2025}
}
Comments
4 pages, 1 figure, 2 tabels, submitted to ERRC/WRSeg 2025