Veil-Then-Reveal: Protecting PII Privacy via Reversible Pseudo-Anonymization in Zero-Trust User--LLM Interaction
A zero-trust pseudo-anonymization layer strips PII before prompts reach remote LLM providers and reversibly restores it locally, reducing logging and breach exposure for regulated enterprises.
Summary written by editorial AI · Source link below
arXiv:2610.22800v1 Announce Type: new Abstract: Large language models (LLMs) increasingly process sensitive documents and personal information, yet sending prompts containing personally identifiable information (PII) to remote providers exposes data subjects and organizations to risks from logging, retention, model training, and unintended disclosure. Existing anonymization methods often mask or remove PII, while conventional encryption eliminates the lexical and structural cues needed for effe
Editorial Analysis
European enterprises sending sensitive data to hosted LLM services face GDPR exposure; reversible anonymization could bridge the gap between utility and compliance.
Evaluate PII-stripping proxy architectures for any workflow that sends personal data to third-party LLM APIs.
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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