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Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations

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arXiv:2606. 15335v1 Announce Type: new Abstract: When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as formatting conventions, vocabulary choices, and syntactic patterns.

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Authors: Xuan Liu, Hefeng Zhou, Sicheng Chen, Chao Yang, Xingcheng Xu, Jingjing Qu, Jiong Lou, Jie LI, Xia Hu
Released: 2026-06-17 (22d ago)
Topic: Related to current project
Relation to my work: Read next because Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, under, alignment, distributional, source, token, rate. Source: arxiv cs.CL (NLP).
arXiv: 2606.15335

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