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Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation by Wei, Yuecen; Fu, Xingcheng; Sun, Qingyun; Peng, Hao; Wu, Jia; Wang, Jinyan; Li, Xianxian is a scholarly article available to read on EtoBox.

What is Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation about?

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we

Author

Wei, Yuecen; Fu, Xingcheng; Sun, Qingyun; Peng, Hao; Wu, Jia; Wang, Jinyan; Li, Xianxian

Published

2022

Language

EN