6 papers
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
Heng Xu, Tianqing Zhu, Dayong Ye +3
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extens…
Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure
Lulu Xue, Shengshan Hu, Linqiang Qian +6
Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects t…
Who's the Mole? Modeling and Detecting Intention-Hiding Malicious Agents in LLM-Based Multi-Agent Systems
Yizhe Xie, Congcong Zhu, Xinyue Zhang +4
Multi-agent systems powered by Large Language Models (LLM-MAS) have demonstrated remarkable capabilities in collaborative problem-solving. However, their deployment also introduces…
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI
Dayong Ye, Tianqing Zhu, Shang Wang +4
Generative AI technology has become increasingly integrated into our daily lives, offering powerful capabilities to enhance productivity. However, these same capabilities can be ex…
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Dayong Ye, Tianqing Zhu, Jiayang Li +5
Duplication is a prevalent issue within datasets. Existing research has demonstrated that the presence of duplicated data in training datasets can significantly influence both mode…
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
Shuai Zhou, Dayong Ye, Tianqing Zhu +1
Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed…