AnonShield: Scalable On-Premise Pseudonymization for CSIRT Vulnerability Data
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Abstract (arXiv)
We present AnonShield, a high-throughput, on-premise pseudonymization system that combines GPU-accelerated NER, streaming processing, caching, and schema-aware configuration. Evaluated on datasets up to 550 MB (70,951 records), AnonShield reduces processing time from over 92 hours to under 10 minutes (up to 738x speedup) while achieving up to 94.2% F1-score and 96.7% recall. Our results show that scalable pseudonymization of vulnerability data is feasible without sacrificing analytical utility, enabling compliant data sharing in operational CSIRT environments.
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References · 3
- 2021k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifierscited by 12
- 2025AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizerscited by 1
- 2025Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMscited by 1
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