5 papers
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
Jie Fu, Nima Naderloui, Da Zhong +2
Machine unlearning (MU) has emerged as a key mechanism for ensuring data privacy and regulatory compliance by enabling models to forget specific training samples. However, recent s…
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…
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…
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling +6
In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de…
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…