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AnonShield leverages GPU-accelerated Named Entity Recognition (NER) and streaming architectures to enable rapid, on-premise pseudonymization of vulnerability data within Computer Security Incident Response Teams (CSIRT). By achieving a 738x speedup while maintaining high analytical utility, this system demonstrates how AI-driven NLP techniques can solve critical bottlenecks in secure data sharing without relying on cloud infrastructure.
AnonShield: Scalable On-Premise Pseudonymization for CSIRT Vulnerability Data
arXiv:2606.15650v1 Announce Type: cross Abstract: 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.
Abstract: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.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Performance (cs.PF)
I.2; C.4; D.2; E.2
arXiv:2606.15650 [cs.CR]
(or arXiv:2606.15650v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.15650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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