Personalized Privacy Control in LLMs via Attention Head Intervention
Quick summary
arXiv:2608.21209v1 Announce Type: new 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}reserva
Key takeaways
- arXiv:2608.21209v1 Announce Type: new 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.
Why it matters
“Personalized Privacy Control in LLMs via Attention Head Intervention” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
Member comments