Mask-Free Privacy Extraction and Rewriting: A Domain-Aware Approach via Prototype Learning

• URL: http://arxiv.org/abs/2604.10145v1
• Date: Sat, 11 Apr 2026 10:21:00 GMT
• Title: Mask-Free Privacy Extraction and Rewriting: A Domain-Aware Approach via Prototype Learning
• Abstract summary: We propose DAMPER (Domain-Aware Mask-free Privacy Extraction and Rewriting) for privacy-sensitive domains.<n>Damper operationalizes latent privacy semantics into compact Domain Privacy Prototypes via contrastive learning.<n>It integrates a sampling-based Exponential Mechanism to provide rigorous span-level Differential Privacy guarantees.
• Score: 13.300036863281996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Client-side privacy rewriting is crucial for deploying LLMs in privacy-sensitive domains. However, existing approaches struggle to balance privacy and utility. Full-text methods often distort context, while span-level approaches rely on impractical manual masks or brittle static dictionaries. Attempts to automate localization via prompt-based LLMs prove unreliable, as they suffer from unstable instruction following that leads to privacy leakage and excessive context scrubbing. To address these limitations, we propose DAMPER (Domain-Aware Mask-free Privacy Extraction and Rewriting). DAMPER operationalizes latent privacy semantics into compact Domain Privacy Prototypes via contrastive learning, enabling precise, autonomous span localization. Furthermore, we introduce a Prototype-Guided Preference Alignment, which leverages learned prototypes as semantic anchors to construct preference pairs, optimizing a domain-compliant rewriting policy without human annotations. At inference time, DAMPER integrates a sampling-based Exponential Mechanism to provide rigorous span-level Differential Privacy (DP) guarantees. Extensive experiments demonstrate that DAMPER significantly outperforms existing baselines, achieving a superior privacy-utility trade-off.

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