You Only Anonymize What Is Not Intent-Relevant: Suppressing Non-Intent Privacy Evidence

TL;DR

IntentAnony is a novel anonymization method that selectively suppresses non-intent cues in user text, improving privacy while maintaining semantic and pragmatic utility, outperforming previous approaches.

Contribution

It introduces intent-conditioned exposure control for text anonymization, modeling pragmatic intent and selectively suppressing non-essential information to enhance privacy-utility balance.

Findings

30% improvement in privacy-utility trade-off

Stronger usability of anonymized text compared to prior methods

Effective suppression of non-intent inference pathways

Abstract

Anonymizing sensitive information in user text is essential for privacy, yet existing methods often apply uniform treatment across attributes, which can conflict with communicative intent and obscure necessary information. This is particularly problematic when personal attributes are integral to expressive or pragmatic goals. The central challenge lies in determining which attributes to protect, and to what extent, while preserving semantic and pragmatic functions. We propose IntentAnony, a utility-preserving anonymization approach that performs intent-conditioned exposure control. IntentAnony models pragmatic intent and constructs privacy inference evidence chains to capture how distributed cues support attribute inference. Conditioned on intent, it assigns each attribute an exposure budget and selectively suppresses non-intent inference pathways while preserving intent-relevant content, semantic structure, affective nuance, and interactional function. We evaluate IntentAnony using privacy inference success rates, text utility metrics, and human evaluation. The results show an approximately 30% improvement in the overall privacy--utility trade-off, with notably stronger usability of anonymized text compared to prior state-of-the-art methods. Our code is available at https://github.com/Nevaeh7/IntentAnony.