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

arXiv Security Archived 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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    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 Focus to learn more Submission history From: Xiaodong Li [view email] [v1] Sat, 11 Apr 2026 10:21:00 UTC (1,969 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 14, 2026
    Archived
    Apr 14, 2026
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