Understanding Fairness–Privacy Interactions in Graph Neural Networks: A Literature Review, Taxonomy, and Open Challenges

Abstract

Graph Neural Networks (GNNs) are increasingly applied to socially consequential tasks, such as disease surveillance on contact graphs, and credit scoring and fraud detection on financial transaction networks. In these settings, biased or unfair predictions can restrict access to opportunities and resources, while privacy leakage can expose sensitive information, leading to substantial individual and societal harms. Consequently, fairness and privacy have become central concerns for trustworthy GNN deployment. Fairness-aware GNNs seek to reduce biased predictions across sensitive entities, whereas privacy-preserving GNNs aim to protect private graph information. However, these objectives are often interdependent: fairness interventions can affect privacy outcomes, while privacy mechanisms may impose additional constraints on fairness. This paper systematically reviews 19 studies to investigate the interplay between fairness and privacy in GNNs. We propose a two-dimensional orthogonal taxonomy that organizes existing work according to fairness objective and privacy target, and use it to analyze dominant research patterns as well as the synergies and trade-offs between fairness and privacy. This is the first review to provide a structured analysis of fairness–privacy interactions in GNNs and to identify open challenges and future research directions for trustworthy graph learning systems.