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 CSI
The increasing volume and sensitivity of cybersecurity vulnerability data necessitate robust, efficient, and compliant pseudonymization solutions to enable essential sharing and analysis without compromising privacy.
This development addresses a critical bottleneck in cybersecurity information sharing, allowing organizations to leverage valuable vulnerability data for defensive purposes while adhering to stringent privacy regulations.
Previously challenging to scale, on-premise pseudonymization of complex vulnerability data is now demonstrated as feasible and highly efficient, enabling more timely and extensive data collaboration.
- · CSIRT/Security teams
- · Data privacy compliance solutions
- · GPU manufacturers
- · AI-powered data processing
- · Attackers relying on data siloing
- · Organizations with poor data governance
- · Manual data anonymization processes
Significantly faster and more compliant processing of sensitive cybersecurity incident data becomes widely accessible.
Improved collaboration and AI-driven analysis of anonymized vulnerability data lead to enhanced collective cybersecurity defenses across industries.
The reduced risk of data breaches from shared vulnerability information encourages greater transparency and proactive security measures, potentially establishing new industry standards for data handling.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI