Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks

arXiv:2301.12951v2 Announce Type: replace
Abstract: Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have emerged as critical considerations. Previous studies have separately examined the fairness and privacy aspects of GNNs, revealing their trade-off with GNN performance. Yet, the interplay between these two aspects remains unexplored. In this paper, we pioneer the exploration of the interaction between the privacy risks of edge leakage and …

abstract applications arxiv build cs.cy cs.lg fairness gnns graph graph neural networks networks neural networks privacy risks studies trade trade-off trustworthy type world