4 citations · 4 across the 6 of their papers we have counts for
6 papers
Membership Inference Attacks Against Fine-tuned Diffusion Language Models
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to priva…
Window-based Membership Inference Attacks Against Fine-tuned Large Language Models
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes…
Imitative Membership Inference Attack
Yuntao Du, Yuetian Chen, Hanshen Xiao +2
A Membership Inference Attack (MIA) assesses how much a target machine learning model reveals about its training data by determining whether specific query instances were part of t…
Cascading and Proxy Membership Inference Attacks
Yuntao Du, Jiacheng Li, Yuetian Chen +5
A Membership Inference Attack (MIA) assesses how much a trained machine learning model reveals about its training data by determining whether specific query instances were included…
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling
Kaiyuan Zhang, Siyuan Cheng, Guangyu Shen +5
Federated learning collaboratively trains a neural network on a global server, where each local client receives the current global model weights and sends back parameter updates (g…
MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
Jiacheng Li, Ninghui Li, Bruno Ribeiro
In Member Inference (MI) attacks, the adversary try to determine whether an instance is used to train a machine learning (ML) model. MI attacks are a major privacy concern when usi…