Graph embeddings have been proposed to map graph data to low dimensional
space for downstream processing (e.g., node classification or link prediction).
With the increasing collection of personal data, graph embeddings can be
trained on private and sensitive data. For the first time, we quantify the
privacy leakage in graph embeddings through three inference attacks targeting
Graph Neural Networks. We propose a membership inference attack to infer
whether a graph node corresponding to individual user’s data was member of the
model’s training or not. We consider a blackbox setting where the adversary
exploits the output prediction scores, and a whitebox setting where the
adversary has also access to the released node embeddings. This attack provides
an accuracy up to 28% (blackbox) 36% (whitebox) beyond random guess by
exploiting the distinguishable footprint between train and test data records
left by the graph embedding. We propose a Graph Reconstruction attack where the
adversary aims to reconstruct the target graph given the corresponding graph
embeddings. Here, the adversary can reconstruct the graph with more than 80% of
accuracy and link inference between two nodes around 30% more confidence than a
random guess. We then propose an attribute inference attack where the adversary
aims to infer a sensitive attribute. We show that graph embeddings are strongly
correlated to node attributes letting the adversary inferring sensitive
information (e.g., gender or location).

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