- Inference Attacks Against Graph Neural Network (2022 USENIX)
- Stealing Links from Graph Neural Networks (2021 USENIX)
- Authors: Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, Yang Zhang
- Attacks: Link Inference
- [paper]
- Node-Level Membership Inference Attacks Against Graph Neural Networks (2021 arXiv)
- Authors: Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, Yang Zhang
- Attacks: Membership Inference
- [paper]
- Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications (2021 ICDM)
- Quantifying Privacy Leakage in Graph Embedding (2020 MobiQuitous and NeurIPS PPML)
- Inference Attacks Against Graph Neural Network (2022 USENIX)
- NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data (2021 TKDE)
- Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective (2021 KDD)
- Authors: Binghui Wang, Jiayi Guo, Ang Li, Yiran Chen, Hai Li
- Defenses: Adversarial Training
- [paper]
- Graph Embedding for Recommendation against Attribute Inference Attacks (2021 WWW)
- Authors: Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang
- Defenses: Differential Privacy
- [paper]
- Information Obfuscation of Graph Neural Networks (ICML 2021)
- Personalized privacy protection in social networks through adversarial modeling (2021 AAAI)
- Authors: Sachin Biradar, Elena Zheleva
- Defenses: Adversarial Training
- [paper]
- Adversarial Privacy Preserving Graph Embedding Against Inference Attack (2020 IoTJ)