{"slug": "demystifying-the-privacy-utility-trade-off-in-llm-interactions", "title": "Demystifying the Privacy-Utility Trade-off in LLM Interactions", "summary": "A new arXiv paper (2609.10992v1) analyzes the privacy-utility trade-off in Large Language Model interactions and introduces an intent-driven local protection framework built on a distilled lightweight model, Veilmind-4B. The framework drives a dynamic extraction-sanitization-restoration pipeline that the authors report reaches a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines. The analysis identifies three mechanisms — Context-Dependent Utility, Strategic Adaptation, and Combinatorial Interplay — governing how sanitization affects downstream performance.", "body_md": "arXiv:2609.10992v1 Announce Type: new \nAbstract: 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. To address this, we conduct a systematic analysis to deconstruct the privacy-utility trade-off, uncovering three underlying mechanisms: (1) Context-Dependent Utility, which first establishes when to sanitize by revealing that data value shifts from critical constraints to dispensable noise based on user intent; (2) Strategic Adaptation, which subsequently determines how to sanitize by dictating that the choice between removal and replacement depends on the task's reliance on factual integrity versus structural coherence; and (3) Combinatorial Interplay, which finally extends the protection scope by demonstrating that attributes form a semantic web of synergistic dependencies or antagonistic redundancies. Guided by these insights, we introduce an intent-driven local protection framework. By distilling a lightweight model Veilmind-4B to drive a dynamic extraction-sanitization-restoration pipeline, our approach reaches a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines, advancing the privacy-utility trade-off toward the Pareto frontier.", "url": "https://wpnews.pro/news/demystifying-the-privacy-utility-trade-off-in-llm-interactions", "canonical_source": "https://arxiv.org/abs/2609.10992", "published_at": "2026-09-12 04:00:00+00:00", "updated_at": "2026-09-12 04:26:54.586145+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research", "ai-ethics"], "entities": ["Veilmind-4B", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/demystifying-the-privacy-utility-trade-off-in-llm-interactions", "markdown": "https://wpnews.pro/news/demystifying-the-privacy-utility-trade-off-in-llm-interactions.md", "text": "https://wpnews.pro/news/demystifying-the-privacy-utility-trade-off-in-llm-interactions.txt", "jsonld": "https://wpnews.pro/news/demystifying-the-privacy-utility-trade-off-in-llm-interactions.jsonld"}}