Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine
An open bilingual benchmark and engine lets enterprises measure how reliably their reversible pseudonymisation protects personal data before documents reach external LLMs — a concrete Art. 25 tool.
Summary written by editorial AI · Source link below
arXiv:2610.34012v1 Announce Type: cross 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 present an open benchmark of five existing open tools and a regular-expression baseline on Russian and UK English, and an open engine built on the two strongest tools. The benchmark excludes each tool's own authors' dataset
Editorial Analysis
Many European organisations already pseudonymise documents before sending them to LLM providers but lack objective metrics; this benchmark enables measurable GDPR compliance evidence.
Evaluate the open benchmark against your current pseudonymisation tooling and integrate results into your DPIA documentation.
An open benchmark now quantifies how well pseudonymisation shields personal data sent to AI services — useful for GDPR accountability.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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