Model Inversion Attacks against Graph Neural Networks. (arXiv:2209.07807v1 [cs.LG])

Many data mining tasks rely on graphs to model relational structures among
individuals (nodes). Since relational data are often sensitive, there is an
urgent need to evaluate the privacy risks in graph data. One famous privacy
attack against data analysis models is the model inversion attack, which aims
to infer sensitive data in the training dataset and leads to great privacy
concerns. Despite its success in grid-like domains, directly applying model
inversion attacks on non-grid domains such as graph leads …

attacks networks neural networks