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New engine and benchmark improve document pseudonymization for LLMs

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces an open benchmark and engine for reversible pseudonymization of documents sent to external large language models. The benchmark evaluates five existing tools and a new engine on Russian and UK English datasets, testing their ability to replace personal data with placeholders and restore it accurately in responses. While existing tools failed to meet predefined thresholds for identifier and leak detection, the new engine demonstrated significantly higher precision, leaving minimal visible data and restoring over 99% of placeholders in UK English, though it faced challenges with Russian name restoration due to inflectional variations. AI

IMPACT Enhances data privacy for users interacting with external LLMs, potentially increasing adoption of sensitive document processing.

RANK_REASON Research paper detailing a new benchmark and engine for document pseudonymization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New engine and benchmark improve document pseudonymization for LLMs

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Research paper detailing a new benchmark and engine for document pseudonymization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Vladimir Beskorovainyi ·

    Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine

    arXiv:2610.34012v1 Announce Type: new Abstract: Professionals send working documents to external large language models. A common safeguard is reversible pseudonymisation: personal data are replaced with placeholders before the request and restored locally in the answer. We presen…

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