Saved in:
| Main Authors: | Wang, Haodi, Jiang, Tangyu, Guo, Yu, Cai, Chengjun, Wang, Cong, Jia, Xiaohua |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.11663 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
P$^2$RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-$k$ Retrieval
by: Ming, Yulong, et al.
Published: (2026)
by: Ming, Yulong, et al.
Published: (2026)
Towards Provable (In)Secure Model Weight Release Schemes
by: Yang, Xin, et al.
Published: (2025)
by: Yang, Xin, et al.
Published: (2025)
Provably Secure Agent Guardrail
by: Wu, Benlong, et al.
Published: (2026)
by: Wu, Benlong, et al.
Published: (2026)
Secure and Privacy-Preserving Vertical Federated Learning
by: Jin, Shan, et al.
Published: (2026)
by: Jin, Shan, et al.
Published: (2026)
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
by: Piran, Fardin Jalil, et al.
Published: (2025)
by: Piran, Fardin Jalil, et al.
Published: (2025)
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
by: Xu, Runhua, et al.
Published: (2025)
by: Xu, Runhua, et al.
Published: (2025)
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
by: Koga, Tatsuki, et al.
Published: (2024)
by: Koga, Tatsuki, et al.
Published: (2024)
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
by: Xu, Jie, et al.
Published: (2024)
by: Xu, Jie, et al.
Published: (2024)
Provably Secure Retrieval-Augmented Generation
by: Zhou, Pengcheng, et al.
Published: (2025)
by: Zhou, Pengcheng, et al.
Published: (2025)
Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)
Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption
by: Ye, Huanyi, et al.
Published: (2026)
by: Ye, Huanyi, et al.
Published: (2026)
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
by: Choquette-Choo, Christopher A., et al.
Published: (2023)
by: Choquette-Choo, Christopher A., et al.
Published: (2023)
BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning
by: Perera, Wadduwage Shanika, et al.
Published: (2025)
by: Perera, Wadduwage Shanika, et al.
Published: (2025)
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillance
by: Ayana, Jifar Wakuma, et al.
Published: (2025)
by: Ayana, Jifar Wakuma, 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)
MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare
by: Yesmin, Farjana
Published: (2025)
by: Yesmin, Farjana
Published: (2025)
Privacy-Preserving LLMs Routing
by: Wu, Xidong, et al.
Published: (2026)
by: Wu, Xidong, et al.
Published: (2026)
VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
by: Cai, Nina, et al.
Published: (2025)
by: Cai, Nina, et al.
Published: (2025)
Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions
by: Hillebrand, Quentin, et al.
Published: (2023)
by: Hillebrand, Quentin, et al.
Published: (2023)
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)
by: Piran, Fardin Jalil, et al.
Published: (2024)
AESP: A Human-Sovereign Economic Protocol for AI Agents with Privacy-Preserving Settlement
by: Wang, Jian Sheng
Published: (2026)
by: Wang, Jian Sheng
Published: (2026)
MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents
by: Zhu, Kaijie, et al.
Published: (2025)
by: Zhu, Kaijie, et al.
Published: (2025)
A Survey: Towards Privacy and Security in Mobile Large Language Models
by: Xu, Honghui, et al.
Published: (2025)
by: Xu, Honghui, et al.
Published: (2025)
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
by: Zhang, Chaoyu, et al.
Published: (2024)
by: Zhang, Chaoyu, et al.
Published: (2024)
Privacy Preservation in Gen AI Applications
by: S, Swetha, et al.
Published: (2025)
by: S, Swetha, et al.
Published: (2025)
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
by: Wang, Wenhao, et al.
Published: (2024)
by: Wang, Wenhao, et al.
Published: (2024)
Provable Privacy with Non-Private Pre-Processing
by: Hu, Yaxi, et al.
Published: (2024)
by: Hu, Yaxi, et al.
Published: (2024)
PRSI: Privacy-Preserving Recommendation Model Based on Vector Splitting and Interactive Protocols
by: Cao, Xiaokai, et al.
Published: (2024)
by: Cao, Xiaokai, et al.
Published: (2024)
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
by: Li, Zhengyi, et al.
Published: (2026)
by: Li, Zhengyi, et al.
Published: (2026)
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)
Attack-Aware Noise Calibration for Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2024)
by: Kulynych, Bogdan, et al.
Published: (2024)
PlanTwin: Privacy-Preserving Planning Abstractions for Cloud-Assisted LLM Agents
by: Yu, Guangsheng, et al.
Published: (2026)
by: Yu, Guangsheng, et al.
Published: (2026)
GOD model: Privacy Preserved AI School for Personal Assistant
by: PIN AI Team, et al.
Published: (2025)
by: PIN AI Team, et al.
Published: (2025)
A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly
by: Yao, Yifan, et al.
Published: (2023)
by: Yao, Yifan, et al.
Published: (2023)
Privacy-Preserving Decentralized AI with Confidential Computing
by: Lee, Dayeol, et al.
Published: (2024)
by: Lee, Dayeol, et al.
Published: (2024)
DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
by: Wang, Erchi, et al.
Published: (2026)
by: Wang, Erchi, et al.
Published: (2026)
Towards Privacy-Preserving Code Generation: Differentially Private Code Language Models
by: Catal, Melih, et al.
Published: (2025)
by: Catal, Melih, et al.
Published: (2025)
Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption
by: Wang, Minjie, et al.
Published: (2025)
by: Wang, Minjie, et al.
Published: (2025)
Similar Items
-
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
by: Liu, Yi, et al.
Published: (2025) -
P$^2$RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-$k$ Retrieval
by: Ming, Yulong, et al.
Published: (2026) -
Towards Provable (In)Secure Model Weight Release Schemes
by: Yang, Xin, et al.
Published: (2025) -
Provably Secure Agent Guardrail
by: Wu, Benlong, et al.
Published: (2026) -
Secure and Privacy-Preserving Vertical Federated Learning
by: Jin, Shan, et al.
Published: (2026)