{"type":"ScholarlyArticle","title":"Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification","authors":[{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Jun Zhou","slug":"jun-zhou","given":"Jun","family":"Zhou","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China; Ant Group , Hangzhou , China"},{"name":"Longfei Zheng","slug":"longfei-zheng","given":"Longfei","family":"Zheng","affiliation":"Ant Group , Hangzhou , China"},{"name":"Huiwen Wu","slug":"huiwen-wu","given":"Huiwen","family":"Wu","affiliation":"Ant Group , Hangzhou , China"},{"name":"Lingjuan Lyu","slug":"lingjuan-lyu","given":"Lingjuan","family":"Lyu","affiliation":"Sony AI , Tokyo , Japan"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Macquarie University , Sydney , NSW 2109 , Australia"},{"name":"Bingzhe Wu","slug":"bingzhe-wu","given":"Bingzhe","family":"Wu","affiliation":"Peking University , Beijing , China"},{"name":"Ziqi Liu","slug":"ziqi-liu","given":"Ziqi","family":"Liu","affiliation":"Ant Group , Hangzhou , China"},{"name":"Li Wang","slug":"li-wang","given":"Li","family":"Wang","affiliation":"Ant Group , Hangzhou , China"},{"name":"Xiaolin Zheng","slug":"xiaolin-zheng","given":"Xiaolin","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China; JZTData Technology , Hangzhou , China"}],"abstract":"Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes. High-performance GNN models always depend on both rich features and complete edge information in graph. However, such information could possibly be isolated by different data holders in practice, which is the so-called data isolation problem. To solve this problem, in this paper, we propose VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. We conduct experiments on three benchmarks and the results demonstrate the effectiveness of VFGNN.","doi":"10.24963/ijcai.2022/272","url":"https://ijcai.org/proceedings/2022/272","pdf":"https://ijcai.org/proceedings/2022/0272.pdf","markdown":"https://ijcai.org/proceedings/2022/272.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/272","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":272,"pages":{"first":1959,"last":1965},"keywords":["Uncertainty in AI: Graphical Models","Data Mining: Privacy Preserving Data Mining","Data Mining: Federated Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987014","length_sec":70,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985157","length_sec":341,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martin Abadi, Andy Chu, Ian Goodfel- low, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In CCS, pages 308-318. ACM, 2016.","title":"Deep learning with differential privacy","authors":"Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan Mcmahan, Ilya Mironov, Kunal Talwar, Li Zhang","year":"2016","venue":"CCS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Acar et al., 2018] Abbas Acar, Hidayet Aksu, A Selcuk Uluagac, and Mauro Conti. A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys (CSUR), 51(4):79, 2018.","title":"A survey on homomorphic encryption schemes: Theory and implementation","authors":"Abbas Acar, Hidayet Aksu, Mauro Selcuk Uluagac, Conti","year":"2018","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Balle and Wang, 2018] Borja Balle and Yu-Xiang Wang. Improving the gaussian mechanism for differential pri- vacy: Analytical calibration and optimal denoising. arXiv preprint arXiv:1805.06530, 2018.","title":"Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising","authors":"Borja Balle, Yu-Xiang Wang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Beaver, 1991] Donald Beaver. Efficient multiparty proto- cols using circuit randomization. In Annual International Cryptology Conference, pages 420-432. Springer, 1991.","title":"Efficient multiparty protocols using circuit randomization","authors":"Donald Beaver","year":"1991","venue":"Annual International Cryptology Conference","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2021] Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, and Cheng Hong. When homomorphic en- cryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control. In SIGKDD, pages 2652-2662. ACM, 2021.","title":"When homomorphic encryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control","authors":"Chen","year":"2021","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dwork et al., 2014] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Founda- tions and Trends in Theoretical Computer Science, 9(3- 4):211-407, 2014.","title":"The algorithmic foundations of differential privacy","authors":"Cynthia Dwork, Aaron Roth","year":"2014","venue":"Foundations and Trends in Theoretical Computer Science","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fang et al., 2021] Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, and Benyu Zhang. Large-scale secure XGB for ver- tical federated learning. In CIKM, pages 443-452. ACM, 2021.","title":"Large-scale secure XGB for vertical federated learning","authors":"Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, Benyu Zhang","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gu et al., 2018] Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Ankita Lamba, Dimitrios Pendarakis, and Ian Molloy. Securing input data of deep learning in- ference systems via partitioned enclave execution. arXiv preprint arXiv:1807.00969, 2018.","title":"Securing input data of deep learning inference systems via partitioned enclave execution","authors":"Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Ankita Lamba, Dimitrios Pendarakis, Ian Molloy","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In NeurIPS, pages 1024-1034, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hardy et al., 2017] Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guil- laume Smith, and Brian Thorne. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677, 2017.","title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","authors":"Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2020] Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. Stealing links from graph neural networks. arXiv preprint arXiv:2005.02131, 2020.","title":"Stealing links from graph neural networks","authors":"He","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu et al., 2020] Weihua Hu, Matthias Fey, Marinka Zit- nik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687, 2020.","title":"Open graph benchmark: Datasets for machine learning on graphs","authors":"Hu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lyu et al., 2020] Lingjuan Lyu, Han Yu, Jun Zhao, and Qiang Yang. Threats to federated learning. In Federated Learning, pages 3-16. Springer, 2020.","title":"Threats to federated learning","authors":"Lyu","year":"2020","venue":"Federated Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mohassel and Zhang, 2017] Payman Mohassel and Yupeng Zhang. Secureml: A system for scalable privacy- preserving machine learning. In S&P, pages 19-38, 2017.","title":"Secureml: A system for scalable privacypreserving machine learning","authors":"Payman Mohassel, Yupeng Zhang","year":"2017","venue":"S&P","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Osia et al., 2019] Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Sina Sajadmanesh, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. A hybrid deep learning architecture for privacy-preserving mobile analytics. arXiv preprint arXiv:1703.02952, 2019.","title":"A hybrid deep learning architecture for privacy-preserving mobile analytics","authors":"Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Sina Sajadmanesh, Nicholas D Hamid R Rabiee, Hamed Lane, Haddadi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Sajadmanesh and Gatica-Perez, 2020] Sina Sajadmanesh and Daniel Gatica-Perez. Locally private graph neural networks. CoRR abs/2006.05535, 12, 2020.","title":"Locally private graph neural networks","authors":"Gatica-Perez ; Sajadmanesh, Sina Sajadmanesh, Daniel Gatica-Perez","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sen et al., 2008] Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 29(3):93-93, 2008.","title":"Collective classification in network data","authors":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, Tina Eliassi-Rad","year":"2008","venue":"AI magazine","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Shamir, 1979] Adi Shamir. How to share a secret. Commu- nications of the ACM, 22(11):612-613, 1979.","title":"How to share a secret","authors":"Adi Shamir","year":"1979","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Veličković et al., 2017] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.","title":"Graph attention networks","authors":"Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Vepakomma et al., 2018] Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. Split learn- ing for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564, 2018.","title":"Split learning for health: Distributed deep learning without sharing raw patient data","authors":"Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wagh et al., 2019] Sameer Wagh, Divya Gupta, and Nis- hanth Chandran. Securenn: 3-party secure computation for neural network training. PETs, 1:24, 2019.","title":"Securenn: 3-party secure computation for neural network training","authors":"Sameer Wagh, Divya Gupta, Nishanth Chandran","year":"2019","venue":"PETs","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2019] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596, 2019.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wu et al., 2021] Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925, 2021.","title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation","authors":"Wu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zheng et al., 2021] Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, and Benyu Zhang. As- fgnn: Automated separated-federated graph neural net- work. PPNA, pages 1-13, 2021.","title":"Asfgnn: Automated separated-federated graph neural network","authors":"Zheng","year":"2021","venue":"PPNA","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}