Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization

ArXi:2602.20743v2 Announce Type: replace Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually designed strategies that lack the flexibility to adjust to diverse requirements and often fail to generalize across domains. We