Does Black-box Attribute Inference Attacks on Graph Neural Networks
  Constitute Privacy Risk?
Iyiola E. Olatunji Anmar Hizber Oliver Sihlovec Megha Khosla
Graph neural networks (GNNs) have shown promising results on real-life
datasets and applications, including healthcare, finance, and education.
However, recent studies have shown that GNNs are highly vulnerable to attacks
such as membership inference attack and link reconstruction attack.
Surprisingly, attribute inference attacks has received little attention. In
this paper, we initiate the first investigation into attribute inference attack
where an attacker aims to infer the sensitive user attributes based on her
public or non-sensitive attributes. We ask the question whether black-box
attribute inference attack constitutes a significant privacy risk for
graph-structured data and their corresponding GNN model. We take a systematic
approach to launch the attacks by varying the adversarial knowledge and
assumptions. Our findings reveal that when an attacker has black-box access to
the target model, GNNs generally do not reveal significantly more information
compared to missing value estimation techniques. Code is available.