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

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. We propose DiSan(Disentangled Sanitization), a privacy-preserving sanitization framework and a built-in component of Intern-Shannon for multi-agent collaboration. DiSan uses a two-stream encoder to factorize text into a source-invariant role subspace tha

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ethics.ai (13 June 2026), “Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations,” evidence record 1023, https://ethics.ai/record/1023 (originally published by arXiv).

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