14 citations · 21 across the 26 of their papers we have counts for
6 papers · 1 filter
Privacy Evaluation Benchmarks for NLP Models
Wei Huang, Yinggui Wang, Cen Chen
By inducing privacy attacks on NLP models, attackers can obtain sensitive information such as training data and model parameters, etc. Although researchers have studied, in-depth,…
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…
Inference Attacks Against Face Recognition Model without Classification Layers
Yuanqing Huang, Huilong Chen, Yinggui Wang +1
Face recognition (FR) has been applied to nearly every aspect of daily life, but it is always accompanied by the underlying risk of leaking private information. At present, almost…
A Fast, Performant, Secure Distributed Training Framework For Large Language Model
Wei Huang, Yinggui Wang, Anda Cheng +3
The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data. However, maliciously stealing model parameters and data from the s…