3 papers
cs.LG2026
Adversarial Attacks on Locally Private Graph Neural Networks
Matta Varun, Ajay Kumar Dhakar, Yuan Hong +1
Graph neural network (GNN) is a powerful tool for analyzing graph-structured data. However, their vulnerability to adversarial attacks raises serious concerns, especially when deal…
cs.LG2025
Safeguarding Graph Neural Networks against Topology Inference Attacks
Jie Fu, Yuan Hong, Zhili Chen +1
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, their widespread adoption has raised serious privacy concerns. While…
cs.CR2025
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…