Personalized Privacy Control in LLMs via Attention Head Intervention

2026-08-21

AI Summary

为解决大语言模型中的个性化隐私控制问题，通过引入用户特定披露偏好并采用注意力头干预方法，提高模型对个性化隐私策略的遵循度。

Abstract

The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLMs regulate information disclosure according to context-dependent norms. However, acceptable disclosure boundaries may vary across users even within the same context. To address this limitation, we introduce \textit{personalized privacy}, which incorporates user-specific disclosure preferences into privacy control. We further present P3Bench~(\textbf{P}ersonalized \textbf{P}rivacy \textbf{P}reservation \textbf{Bench}mark), a novel benchmark extending contextual privacy policies with personalized disclosure policies. Experiments show that prompt-based policies fail to reliably enforce personalized privacy policies, with Qwen2.5-7B and Gemma3-4B showing average policy ignorance ratios of 51.25\% and 74.28\%, respectively. Finally, to address this problem, we propose \textsc{Repair}, a robust inference-time attention head intervention method that adjusts disclosure behavior toward policy-consistent responses. Our method significantly improves adherence to user-specific privacy preferences by reducing cases where the model fails to follow the given policy.

Problem

• Personalized Privacy
• User-specific Preferences
• Privacy Control
• Attention Head Intervention

Innovation

• personalized privacy
• attention head intervention
• P3Bench