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On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Zhanke Zhou · Chenyu Zhou · Xuan Li · Jiangchao Yao · QUANMING YAO · Bo Han

Tue Jul 25 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #723
Event URL: https://github.com/tmlr-group/MC-GRA »

Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the first comprehensive study of graph reconstruction attack that aims to reconstruct the adjacency of nodes. We show that a range of factors in GNNs can lead to the surprising leakage of private links. Especially by taking GNNs as a Markov chain and attacking GNNs via a flexible chain approximation, we systematically explore the underneath principles of graph reconstruction attack, and propose two information theory-guided mechanisms: (1) the chain-based attack method with adaptive designs for extracting more private information; (2) the chain-based defense method that sharply reduces the attack fidelity with moderate accuracy loss. Such two objectives disclose a critical belief that to recover better in attack, you must extract more multi-aspect knowledge from the trained GNN; while to learn safer for defense, you must forget more link-sensitive information in training GNNs. Empirically, we achieve state-of-the-art results on six datasets and three common GNNs. The code is publicly available at: https://github.com/tmlr-group/MC-GRA.

Author Information

Zhanke Zhou (Hong Kong Baptist University)
Chenyu Zhou (Huazhong University of Science and Technology)
Xuan Li (South China Agricultural University)
Jiangchao Yao (Cooperative Medianet Innovation Center, Shang hai Jiao Tong University)
QUANMING YAO (4Paradigm)

Dr. Quanming Yao is currently a leading researcher in 4Paradigm and managing the company's research group. He obtained his Ph.D. degree at the Department of Computer Science and Engineering at Hong Kong University of Science and Technology (HKUST) in 2018 and received his bachelor degree at HuaZhong University of Science and Technology (HUST) in 2013. He is Qiming Star (HUST, 2012), Tse Cheuk Ng Tai Research Excellence Prize (CSE, HKUST, 2014-2015), Google Fellowship (machine learning, 2016) and Ph.D. Research Excellence Award (School of Engineering, HKUST, 2018-2019). He has 23 top-tier journal and conference papers, including ICML, NeurIPS, JMLR, TPAMI, KDD, ICDE, CVPR, IJCAI, and AAAI; he was an outstanding reviewer of Neurocomputing in 2017; served as program committee of many prestigious conferences, including ICML, NeurIPS, CVPR, AAAI, and IJCAI; one of the committees of AutoML competition in NeurIPS-2018, IJCNN-2019 and IJCAI-2019.


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