36 citations · 40 across the 48 of their papers we have counts for
12 papers · 1 filter
Machine Unlearning in Low-Dimensional Feature Subspace
Kun Fang, Qinghua Tao, Junxu Liu +5
Machine Unlearning (MU) aims at removing the influence of specific data from a pretrained model while preserving performance on the remaining data. In this work, a novel perspectiv…
Adversarial Signed Graph Learning with Differential Privacy
Haobin Ke, Sen Zhang, Qingqing Ye +2
Signed graphs with positive and negative edges can model complex relationships in social networks. Leveraging on balance theory that deduces edge signs from multi-hop node pairs, s…
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Tairan Huang, Yulin Jin, Junxu Liu +2
Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existi…
Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Zi Liang, Zhiyao Wu, Haoyang Shang +5
Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties an…
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Yaxin Xiao, Qingqing Ye, Li Hu +5
Machine unlearning enables the removal of specific data from ML models to uphold the right to be forgotten. While approximate unlearning algorithms offer efficient alternatives to…
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
Zi Liang, Haibo Hu, Qingqing Ye +2
Low rank adaptation (LoRA) has emerged as a prominent technique for fine-tuning large language models (LLMs) thanks to its superb efficiency gains over previous methods. While exte…
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
Sen Zhang, Qingqing Ye, Haibo Hu +1
The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training…
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
Haoyang Li, Li Bai, Qingqing Ye +4
Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to re…
FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
Zitong Li, Qingqing Ye, Haibo Hu
Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying w…
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
Jiale Zhang, Bosen Rao, Chengcheng Zhu +6
Graph Neural Networks (GNNs) have achieved remarkable performance through their message-passing mechanism. However, recent studies have highlighted the vulnerability of GNNs to bac…
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Jie Fu, Qingqing Ye, Haibo Hu +4
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and member…