A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications. (arXiv:2308.16375v2 [cs.LG] UPDATED)

Graph Neural Networks (GNNs) have gained significant attention owing to their
ability to handle graph-structured data and the improvement in practical
applications. However, many of these models prioritize high utility
performance, such as accuracy, with a lack of privacy consideration, which is a
major concern in modern society where privacy attacks are rampant. To address
this issue, researchers have started to develop privacy-preserving GNNs.
Despite this progress, there is a lack of a comprehensive overview of the
attacks and the …

accuracy applications attacks attention data high improvement major networks neural networks performance preservation privacy structured data survey utility