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This paper introduces DiSan, a privacy-preserving text sanitization framework for distributed agent collaboration. By disentangling source-invariant role content from source-identifying style, DiSan reduces PII exposure 20× while maintaining 83% answer faithfulness on a multi-agent RAG benchmark, outperforming traditional masking approaches.