97 citations · 98 across the 5 of their papers we have counts for
5 papers
PRAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top- Retrieval
Yulong Ming, Mingyue Wang, Jijia Yang +4
Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy concerns for both data owners and users…
LiveVal: Time-aware Data Valuation via Adaptive Reference Points
Jie Xu, Zihan Wu, Cong Wang +1
Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing met…
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
Jie Xu, Zihan Wu, Cong Wang +1
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \t…
Machine Unlearning: Solutions and Challenges
Jie Xu, Zihan Wu, Cong Wang +1
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradati…
Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process
Sen Peng, Yufei Chen, Cong Wang +1
Diffusion models have rapidly become a vital part of deep generative architectures, given today's increasing demands. Obtaining large, high-performance diffusion models demands sig…