Demystifying the Privacy-Utility Trade-off in LLM Interactions

arXiv:2609.10992v1 Announce Type: new 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. 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.

The item presents a systematic analysis and introduces a framework to improve privacy protection while preserving response utility.

The research clarifies mechanisms of privacy-utility trade-offs in LLM use and proposes a dynamic framework improving privacy protection with better utility, benefiting user privacy and task performance. However, risks remain from potential privacy exposure and complexity in sanitization strategies as integration into daily tasks grows.

This is an incremental technical privacy research contribution with plausible but unverified utility gains, not yet peer reviewed. It has modest positive implications for user data protection if adopted, but limited evidence of real-world deployment or independent validation.

The research proposes a method to improve privacy in LLM interactions without significantly degrading utility, addressing a key challenge in AI adoption. This advance suggests a more secure future for personal data in AI applications. The study provides a systematic analysis and introduces a new framework.

The authors present a framework that improves both privacy and utility in LLM interactions. The evidence is primary and the claims are specific and measurable.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   b53771b0-5313-448e-a37f-9b141385a924
           https://arxiv.org/abs/2609.10992
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +14  (Mixed or uncertain)
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CONFIDENCE 74%