6 papers
LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
Wei Huang, Anda Cheng, Yinggui Wang +2
Large Language Models (LLMs) can be fine-tuned on domain-specific data to enhance their performance in specialized fields. However, such data often contains numerous low-quality sa…
Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
Le Ma, Shirao Yang, Zihao Wang +4
The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, s…
PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models
Guangwei Li, Yuansen Zhang, Yinggui Wang +3
The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is bec…
Information Leakage from Embedding in Large Language Models
Zhipeng Wan, Anda Cheng, Yinggui Wang +1
The widespread adoption of large language models (LLMs) has raised concerns regarding data privacy. This study aims to investigate the potential for privacy invasion through input…
Ditto: Quantization-aware Secure Inference of Transformers upon MPC
Haoqi Wu, Wenjing Fang, Yancheng Zheng +4
Due to the rising privacy concerns on sensitive client data and trained models like Transformers, secure multi-party computation (MPC) techniques are employed to enable secure infe…
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack
Yinggui Wang, Yuanqing Huang, Jianshu Li +3
The utilization of personal sensitive data in training face recognition (FR) models poses significant privacy concerns, as adversaries can employ model inversion attacks (MIA) to i…