Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

1 code implementation • 2 Oct 2022 • Yuecen Wei, Xingcheng Fu, Qingyun Sun, Hao Peng, Jia Wu, Jinyan Wang, Xianxian Li

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.

Privacy Preserving

SSGD: A safe and efficient method of gradient descent

1 code implementation • 3 Dec 2020 • Jinhuan Duan, Xianxian Li, Shiqi Gao, Jinyan Wang, Zili Zhong

In this paper, to prevent gradient leakage while keeping the accuracy of model, we propose the super stochastic gradient descent approach to update parameters by concealing the modulus length of gradient vectors and converting it or them into a unit vector.