Mask-Free Privacy Extraction and Rewriting: A Domain-Aware Approach via Prototype Learning
arXiv SecurityArchived Apr 14, 2026✓ Full text saved
arXiv:2604.10145v1 Announce Type: new 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
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 11 Apr 2026]
Mask-Free Privacy Extraction and Rewriting: A Domain-Aware Approach via Prototype Learning
Xiaodong Li, Yuhua Wang, Qingchen Yu, Zixuan Qin, Yifan Sun, Qinnan Zhang, Hainan Zhang, Zhiming Zheng
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.
Comments: 30 pages,21 figures,11 tables
Subjects: Cryptography and Security (cs.CR)
Cite as: arXiv:2604.10145 [cs.CR]
(or arXiv:2604.10145v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2604.10145
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From: Xiaodong Li [view email]
[v1] Sat, 11 Apr 2026 10:21:00 UTC (1,969 KB)
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