Node Injection Link Stealing Attack
Oualid Zari Javier Parra-Arnau Ayşe Ünsal Melek Önen
In this paper, we present a stealthy and effective attack that exposes
privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private
links within graph-structured data. Focusing on the inductive setting where new
nodes join the graph and an API is used to query predictions, we investigate
the potential leakage of private edge information. We also propose methods to
preserve privacy while maintaining model utility. Our attack demonstrates
superior performance in inferring the links compared to the state of the art.
Furthermore, we examine the application of differential privacy (DP) mechanisms
to mitigate the impact of our proposed attack, we analyze the trade-off between
privacy preservation and model utility. Our work highlights the privacy
vulnerabilities inherent in GNNs, underscoring the importance of developing
robust privacy-preserving mechanisms for their application.