A. Keji

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

This work demonstrates that an adversary can use the generator-discriminator technique to reconstruct high-quality graphs in real-world black-box attack scenarios against GNNs, and shows that GNNs are highly vulnerable to privacy attacks, varying Laplacian noise-scales.