AnonShield On-premise entities anonymization.
Research-grade sensitive data redaction. Zero cloud, zero persistence. Published at SBSeg 2025, ERRC 2025, and SBRC 2026.
Live demo — edit the text runs in-browser, no server
Meeting notes — Q4 Security Review
Attendees: Sarah Chen ([EMAIL-2g07bt]), Marcus Rodriguez
Phone: [PHONE-b0hxs5] · Alt: [PHONE-6i11m8]
Action items:
• Card on file: [CREDIT·CARD-fj28wp] (exp. 09/27)
• Migrate server [IP·ADDRESS-ev73g9] → new host at [IP·ADDRESS-63vl0e]
• Patch [CVE·ID-aa8y9z] before Friday
• API token: [AUTH·TOKEN-x5p27n]
• Leaked URL: [URL-e1ebcm]
• File hash: [HASH-s5gyw4]
PHONE 2
IP_ADDRESS 2
EMAIL 1
CREDIT_CARD 1
CVE_ID 1
AUTH_TOKEN 1
URL 1
HASH 1
How it works
Privacy guaranteed: we process everything without saving anything.
Input TXT PDF DOCX ZIP…
NER Detection Transformer or regex
HMAC-SHA256 Deterministic hash
Pseudonymization HMAC token replaced
Output Deleted after download
Key stored only in Redis (1h TTL) · Deleted by worker immediately after processing
738× faster than baseline
94.2% F1 on CTI dataset
96.7% mean recall
550MB in under 10 min
0 cloud calls
10GB max file size (with key)
Academic validation
Published at peer-reviewed venues
SBSeg 2025
Anonimização de Incidentes de Segurança com Reidentificação Controlada
100% Prec. 97.38% Rec. 763 Incidents
DOI Link ↗ERRC 2025 (WRSeg)
AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizers
100% Precision 92.1% F1 (XML) On-premise
DOI Link ↗SBRC 2026 (Salão de Ferramentas)
AnonShield: Scalable On-Premise Pseudonymization for CSIRT Vulnerability Data
94.2% F1 96.7% Recall 738× Speedup
GitHub Repo ↗Team
Built by researchers
Cristhian Kapelinski UNIPAMPA
Douglas Lautert UNIPAMPA
Beatriz Machado UNIPAMPA
Diego Kreutz UNIPAMPA
Isadora G. Ferrão UBO
Universidade Federal do Pampa (UNIPAMPA) · Université de Bretagne Occidentale (UBO)