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New framework protects PII in LLM interactions via reversible pseudo-anonymization

By PulseAugur Editorial · [1 sources] ·

A new framework called Veil-then-Reveal (VtR) has been proposed to protect Personally Identifiable Information (PII) during interactions with large language models (LLMs). This user-side approach aims to prevent risks associated with sending sensitive data to remote LLM providers by pseudo-anonymizing PII before it leaves the user's device. VtR replaces PII with realistic surrogates while preserving linguistic and structural properties, and allows for reversible restoration of the original PII in LLM responses. AI

IMPACT This framework could enhance privacy for users interacting with LLMs, potentially increasing trust and adoption of LLM technologies for sensitive data processing.

RANK_REASON The cluster contains an academic paper detailing a new technical framework for LLM interaction. [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 framework protects PII in LLM interactions via reversible pseudo-anonymization

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The cluster contains an academic paper detailing a new technical framework for LLM interaction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mohamed Shaaban, Mohamed Elmahallawy, Thomas E. Carroll ·

    Veil-Then-Reveal: Protecting PII Privacy via Reversible Pseudo-Anonymization in Zero-Trust User--LLM Interaction

    arXiv:2610.22800v1 Announce Type: cross 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 t…

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