
Proposing a decoupled framework for training GNN models with enhanced privacy-utility trade-off using DP-APPR algorithms.


coremsg

dpar-decoupled-graph-neural-networks-with-node-level-differential-privacy


DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy


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Eclipse introduces a privacy-preserving GNN training algorithm that perturbs singular values, achieving strong privacy protection on edges while maintaining model utility.


edge-private-graph-neural-networks-with-singular-value-perturbation-privacy-preserving-gnn-training-algorithm-eclipse


Edge Private Graph Neural Networks with Singular Value Perturbation: Privacy-Preserving GNN Training Algorithm Eclipse



Achieving node-level differential privacy for training GNNs with enhanced privacy-utility trade-off.


decoupled-graph-neural-networks-with-node-level-differential-privacy


Decoupled Graph Neural Networks with Node-Level Differential Privacy
