arXiv AI June 4, 2026 By Peihua Mai, Xuanrong Gao, Youlong Ding, Xianglong Du, Wei Liu, Yan Pang

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models

arXiv:2606. 05004v1 Announce Type: cross Abstract: With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 14

PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference

arXiv:2607. 10709v1 Announce Type: cross Abstract: Large Language Model (LLM) services introduce a fundamental privacy challenge.

By Chen Gu, Hui Wan, Donghui Hu, Hui Wang, Zhuoer Gu

llms safety

arXiv Machine Learning

Aug 4

Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning

arXiv:2410. 06814v2 Announce Type: replace Abstract: Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model.

By Hengxiang Zhang, Qiang Hu, Hongxin Wei

efficiency benchmarks safety

arXiv AI

Jun 24

Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

arXiv:2606. 24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts.

By Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang

llms rag agents safety

arXiv Machine Learning

Jul 21

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.

By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

llms nlp safety

arXiv Machine Learning

Jun 9

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

arXiv:2606. 09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees.

By Bart{\l}omiej Marek, Lorenzo Rossi, Vincent Hanke, Xun Wang, Michael Backes, Franziska Boenisch, Adam Dziedzic

llms fine-tuning benchmarks safety

arXiv AI

2d ago

P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems

arXiv:2608. 14094v1 Announce Type: cross Abstract: Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices.

By Myunghoon Ryu, Geunpyo Park, Sungjoon Lee, XinYu Piao, Jong-Kook Kim

llms fine-tuning efficiency benchmarks safety

Related stories

PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference

Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning

Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems