Artificial Intelligence #privacy-preserving#text sanitization
Privacy-Preserving Text Sanitization for Distributed Agents via Disentangled Representations
Researchers propose DiSan, a privacy-preserving text sanitization framework that uses disentangled representations to separate task semantics from style identifiers. Experiments show it reduces personally identifiable information exposure by 20 times while maintaining 83% answer faithfulness on a multi-agent RAG benchmark, outperforming token-level masking.
Jun 16, 2026 1 source
