5 papers
Unreal Thinking: Chain-of-Thought Hijacking via Two-stage Backdoor
Wenhan Chang, Tianqing Zhu, Ping Xiong +2
Large Language Models (LLMs) are increasingly deployed in settings where Chain-of-Thought (CoT) is interpreted by users. This creates a new safety risk: attackers may manipulate th…
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
Faqian Guan, Tianqing Zhu, Zhoutian Wang +2
With increasing concerns about privacy attacks and potential sensitive information leakage, researchers have actively explored methods to efficiently remove sensitive training data…
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
Wenhan Chang, Tianqing Zhu, Ping Xiong +3
In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specif…
Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
Faqian Guan, Tianqing Zhu, Wenhan Chang +2
Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a…
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
Faqian Guan, Tianqing Zhu, Hui Sun +2
Graph data contains rich node features and unique edge information, which have been applied across various domains, such as citation networks or recommendation systems. Graph Neura…