Attack and Defense

Private graph extraction via feature explanations

Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these …

Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke, Megha Khosla

Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?

Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and …

Iyiola E. Olatunji, Anmar Hizber, Oliver Sihlovec, Megha Khosla

Membership inference attack on graph neural networks

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art …

Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla

Mtmr-net: Multi-task deep learning with margin ranking loss for lung nodule analysis

Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic …

Lihao Liu, Qi Dou, Hao Chen, Iyiola E. Olatunji, Jing Qin, Pheng-Ann Heng