Demystifying the Privacy-Utility Trade-off in LLM Interactions: The Veilmind-4B Framework

Forum topic · 小凯 · 2026-09-13

Summary

This forum post introduces an arXiv paper (2609.10992) by Zhenhua Liu, Zhanxu Xie, Junjie Yu, Tong Zhu, Lijun Li, and Wenliang Chen on the privacy-utility trade-off in Large Language Model interactions. Using context-rich instructions exposes sensitive user information, yet current privacy-preserving methods rely on context-agnostic static rules that severely degrade utility. The authors systematically analyze how sanitization affects downstream performance and uncover three mechanisms: (1) Context-Dependent Utility—data value shifts from critical constraint to dispensable noise based on user intent; (2) Strategic Adaptation—the choice between removal and replacement depends on whether a task requires factual integrity or structural coherence; (3) Combinatorial Interplay—attributes form semantic webs of synergistic dependencies or antagonistic redundancies. Based on these insights, the paper proposes an intent-driven local protection framework using a distilled lightweight model, Veilmind-4B, powering a dynamic extraction-sanitization-restoration pipeline. It achieves low information leakage while preserving substantially higher response utility than existing privacy baselines, pushing the privacy-utility trade-off toward the Pareto frontier.

Paper Overview

Research areas: cs.AI, cs.CR Authors: Zhenhua Liu, Zhanxu Xie, Junjie Yu, Tong Zhu, Lijun Li, Wenliang Chen Published: 2026-09-13 arXiv: 2609.10992

Abstract

The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules, causing severe utility degradation. However, the specific mechanisms governing how sanitization impacts downstream performance remain largely underexplored.

Key Findings

The authors conduct a systematic analysis to deconstruct the privacy-utility trade-off, uncovering three underlying mechanisms:

1. Context-Dependent Utility — establishes *when* to sanitize by revealing that data value shifts from critical constraint to dispensable noise based on user intent. 2. Strategic Adaptation — determines *how* to sanitize, dictating that the choice between removal and replacement depends on the task's reliance on factual integrity versus structural coherence. 3. Combinatorial Interplay — extends the protection scope by demonstrating that attributes form a semantic web of synergistic dependencies or antagonistic redundancies.

Proposed Framework

Guided by these insights, the paper introduces an intent-driven local protection framework:

Distills a lightweight model, Veilmind-4B, to drive a dynamic extraction-sanitization-restoration pipeline.
Achieves a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines.
Advances the privacy-utility trade-off toward the Pareto frontier.

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