36 citations · 38 across the 31 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…