{"ID":2840224,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15744","arxiv_id":"2511.15744","title":"AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizers","abstract":"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.","short_abstract":"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 a...","url_abs":"https://arxiv.org/abs/2511.15744","url_pdf":"https://arxiv.org/pdf/2511.15744v1","authors":"[\"Cristhian Kapelinski\",\"Douglas Lautert\",\"Beatriz Machado\",\"Diego Kreutz\"]","published":"2025-11-18T20:34:49Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}