Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference

Reported by arXiv cs.CR (Mingchen Li, Zifan Peng, Kewei Sha, Lavinia F Pieptea, Heng Fan, Junhua Ding, Meikang Qiu, Yunhe Feng)

Summary by Cyber Security News21 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.27213

arXiv:2610.27213v1 Announce Type: new Abstract: Personalized large language models (LLMs) increasingly rely on profile-conditioned inference, pairing public requests with rich, user-specific facts. This paradigm exposes the user profile itself, together with the user's explicit sharing preferences. We introduce Profile-DP, a policy-aware local differential privacy framework designed specifically for user-profile release in personalized LLM inference.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

Related coverage

PrivacyEvalClaw: A Tool-Augmented Deliberative Agent for Auditing Privacy Leakage in LLM Jailbreak Responses

TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs

Label-Budgeted Attribute Inference from Protected Features in Edge-Cloud Split Multimodal LLMs

Guided Diffusion for Trustworthy Synthetic Text Generation

The LLM-Attacker: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems

Veil-Then-Reveal: Protecting PII Privacy via Reversible Pseudo-Anonymization in Zero-Trust User--LLM Interaction