Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
Fuente:
arXiv
Saved in:
| Main Authors: | Yu, Honglan, Wang, Yibin, Dai, Feifei, Liu, Dong, Fan, Haihui, Gu, Xiaoyan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Privacy-Preserving Decentralized AI with Confidential Computing
by: Lee, Dayeol, et al.
Published: (2024)
by: Lee, Dayeol, et al.
Published: (2024)
WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference
by: Xiong, Zixun, et al.
Published: (2026)
by: Xiong, Zixun, et al.
Published: (2026)
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
by: Lin, Yu, et al.
Published: (2026)
by: Lin, Yu, et al.
Published: (2026)
No Free Lunch Theorem for Privacy-Preserving LLM Inference
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents
by: Wang, Shouju, et al.
Published: (2025)
by: Wang, Shouju, et al.
Published: (2025)
A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality
by: Huang, Hanbo, et al.
Published: (2024)
by: Huang, Hanbo, et al.
Published: (2024)
Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design
by: Ben, Dong, et al.
Published: (2025)
by: Ben, Dong, et al.
Published: (2025)
CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection
by: Hu, Jiaming, et al.
Published: (2025)
by: Hu, Jiaming, et al.
Published: (2025)
Dstack: A Zero Trust Framework for Confidential Containers
by: Zhou, Shunfan, et al.
Published: (2025)
by: Zhou, Shunfan, et al.
Published: (2025)
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
by: Yang, Haowei, et al.
Published: (2025)
by: Yang, Haowei, et al.
Published: (2025)
LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance
by: Wang, Yu, et al.
Published: (2025)
by: Wang, Yu, et al.
Published: (2025)
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration
by: Zeng, Ziqian, et al.
Published: (2024)
by: Zeng, Ziqian, et al.
Published: (2024)
SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
by: Zhang, Kaiyuan, et al.
Published: (2025)
by: Zhang, Kaiyuan, et al.
Published: (2025)
Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark
by: Cai, Yuhang, et al.
Published: (2025)
by: Cai, Yuhang, et al.
Published: (2025)
Depth Gives a False Sense of Privacy: LLM Internal States Inversion
by: Dong, Tian, et al.
Published: (2025)
by: Dong, Tian, et al.
Published: (2025)
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models
by: Huang, Chung-ju, et al.
Published: (2026)
by: Huang, Chung-ju, et al.
Published: (2026)
TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents
by: Liu, Yibing, et al.
Published: (2026)
by: Liu, Yibing, et al.
Published: (2026)
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
by: Li, Yang, et al.
Published: (2024)
by: Li, Yang, et al.
Published: (2024)
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation
by: Yoon, Jeongho, et al.
Published: (2026)
by: Yoon, Jeongho, et al.
Published: (2026)
Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning
by: Li, Qi, et al.
Published: (2024)
by: Li, Qi, et al.
Published: (2024)
Lost in Modality: Evaluating the Effectiveness of Text-Based Membership Inference Attacks on Large Multimodal Models
by: Tong, Ziyi, et al.
Published: (2025)
by: Tong, Ziyi, et al.
Published: (2025)
Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks
by: Jin, Haotian, et al.
Published: (2025)
by: Jin, Haotian, et al.
Published: (2025)
A First Look At Efficient And Secure On-Device LLM Inference Against KV Leakage
by: Yang, Huan, et al.
Published: (2024)
by: Yang, Huan, et al.
Published: (2024)
Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
by: Kotal, Anantaa, et al.
Published: (2023)
by: Kotal, Anantaa, et al.
Published: (2023)
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
by: Negoya, Yusaku, et al.
Published: (2025)
by: Negoya, Yusaku, et al.
Published: (2025)
Protecting Private Code in IDE Autocomplete using Differential Privacy
by: Grigorenko, Evgeny, et al.
Published: (2026)
by: Grigorenko, Evgeny, et al.
Published: (2026)
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis
by: Tan, Xue, et al.
Published: (2024)
by: Tan, Xue, et al.
Published: (2024)
To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt
by: Wang, Zhilong, et al.
Published: (2025)
by: Wang, Zhilong, et al.
Published: (2025)
Unveiling Privacy Risks in LLM Agent Memory
by: Wang, Bo, et al.
Published: (2025)
by: Wang, Bo, et al.
Published: (2025)
Secure Confidential Business Information When Sharing Machine Learning Models
by: Yang, Yunfan, et al.
Published: (2025)
by: Yang, Yunfan, et al.
Published: (2025)
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
by: Fu, Haowei, et al.
Published: (2025)
by: Fu, Haowei, et al.
Published: (2025)
When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI
by: Forough, Javad, et al.
Published: (2026)
by: Forough, Javad, et al.
Published: (2026)
Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inference
by: Luo, Zhifan, et al.
Published: (2025)
by: Luo, Zhifan, et al.
Published: (2025)
Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning
by: Yang, Ze, et al.
Published: (2025)
by: Yang, Ze, et al.
Published: (2025)
Nimbus: Secure and Efficient Two-Party Inference for Transformers
by: Li, Zhengyi, et al.
Published: (2024)
by: Li, Zhengyi, et al.
Published: (2024)
Confidential Prompting: Privacy-preserving LLM Inference on Cloud
by: Li, Caihua, et al.
Published: (2024)
by: Li, Caihua, et al.
Published: (2024)
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2025)
by: Zhang, Xiaojin, et al.
Published: (2025)
LLM-PBE: Assessing Data Privacy in Large Language Models
by: Li, Qinbin, et al.
Published: (2024)
by: Li, Qinbin, et al.
Published: (2024)
FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
by: Ye, Pukang, et al.
Published: (2025)
by: Ye, Pukang, et al.
Published: (2025)
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
by: Lam, Maximilian, et al.
Published: (2022)
by: Lam, Maximilian, et al.
Published: (2022)
Similar Items
-
Privacy-Preserving Decentralized AI with Confidential Computing
by: Lee, Dayeol, et al.
Published: (2024) -
WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference
by: Xiong, Zixun, et al.
Published: (2026) -
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
by: Lin, Yu, et al.
Published: (2026) -
No Free Lunch Theorem for Privacy-Preserving LLM Inference
by: Zhang, Xiaojin, et al.
Published: (2024) -
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents
by: Wang, Shouju, et al.
Published: (2025)