P$$^2$$CG: a privacy preserving collaborative graph neural network training framework

Author:

Miao XupengORCID,Zhang Wentao,Jiang Yuezihan,Fu Fangcheng,Shao Yingxia,Chen Lei,Tao Yangyu,Cao Gang,Cui Bin

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems

Reference103 articles.

1. Tencent Inc. Weshi (2021). https://weishi.qq.com

2. Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: UAI, p. 310 (2019)

3. Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Steeg, G.V., Galstyan, A.: Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research, vol. 97, pp. 21–29. PMLR (2019). http://proceedings.mlr.press/v97/abu-el-haija19a.html

4. Albrecht, J.P.: How the GDPR will change the world. Eur. Data Prot. L. Rev. 2, 287 (2016)

5. Aono, Y., Hayashi, T., Phong, L.T., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: CODASPY, pp. 142–144 (2016). https://doi.org/10.1145/2857705.2857731

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Distributed Graph Representation Learning;Computer Supported Cooperative Work and Social Computing;2024

2. SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service;IEEE Transactions on Services Computing;2023-07-01

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