The paper argues that privacy in personalized AI should be viewed as a system-level issue rather than just a model-level one. It identifies four interconnected privacy‑risk channels in personalized AI and proposes four system‑level requirements—interaction trajectories, internal information flows, indirect leakage, and the privacy‑utility trade‑off—for evaluating privacy. The authors call for these requirements to be systematically incorporated into privacy audits of personalized AI systems.
By Guillaume Salha-Galvan, Jiaying Xu
arXiv:2609.35937v1 Announce Type: cross
Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice o...
By Lucas Biechy, C\'edric Eichler, H\'eber H. Arcolezi, Nicolas Anciaux
arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.
By Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah
arXiv:2607. 13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail.
By Alexandra E. Michael, Franziska Roesner
arXiv:2607. 05363v1 Announce Type: new Abstract: Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services.
By Dylan Zongmin Liu
The paper introduces the concept of personalized privacy for large language models, allowing user‑specific disclosure preferences to guide information sharing. It presents P3Bench, a benchmark that extends contextual privacy policies with personalized rules, and shows that existing prompt‑based methods often ignore these policies. To improve compliance, the authors propose “Repair”, an inference‑time attention head intervention that aligns model responses with user‑specific privacy rules.
By Junseok Kim, Nakyeong Yang, Kyomin Jung