Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks

• URL: http://arxiv.org/abs/2301.12951v2
• Date: Wed, 20 Mar 2024 03:26:28 GMT
• Title: Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
• Authors: He Zhang, Xingliang Yuan, Shirui Pan,
• Abstract summary: 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.
• Score: 66.0143583366533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 the individual fairness of a GNN. Our theoretical analysis unravels that edge privacy risks unfortunately escalate when the nodes' individual fairness improves. Such an issue hinders the accomplishment of privacy and fairness of GNNs at the same time. To balance fairness and privacy, we carefully introduce fairness-aware loss reweighting based on influence function and privacy-aware graph structure perturbation modules within a fine-tuning mechanism. Experimental results underscore the effectiveness of our approach in achieving GNN fairness with limited performance compromise and controlled privacy risks. This work contributes to the comprehensively developing trustworthy GNNs by simultaneously addressing both fairness and privacy aspects.

Related papers

• GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models [3.0509197593879844]
  This paper investigates edge privacy in contexts where adversaries possess black-box GNN model access. We introduce a series of privacy attacks grounded on the message-passing mechanism of GNNs.
  arXiv  Detail & Related papers  (2023-11-03T20:26:03Z)
• Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation [79.64626707978418]
  We propose using link local differential privacy over decentralized nodes to train graph neural networks. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology. Our approach outperforms existing methods in terms of accuracy under varying privacy budgets.
  arXiv  Detail & Related papers  (2023-09-06T17:53:31Z)
• A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications [76.88662943995641]
  Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data. 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 techniques for preserving privacy in the graph domain.
  arXiv  Detail & Related papers  (2023-08-31T00:31:08Z)
• Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs [7.626349365968476]
  Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. Privacy-preserving GNNs have been proposed, focusing on preserving node and/or link privacy. We propose a principled privacy-preserving GNN framework that effectively safeguards both node and link privacy.
  arXiv  Detail & Related papers  (2023-08-25T17:46:43Z)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends [115.84291569988748]
  Graph neural networks (GNNs) have emerged as competent graph learning methods for diverse real-world scenarios. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness.
  arXiv  Detail & Related papers  (2022-05-16T02:21:09Z)
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability [59.80140875337769]
  Graph Neural Networks (GNNs) have made rapid developments in the recent years. GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data. This paper gives a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability.
  arXiv  Detail & Related papers  (2022-04-18T21:41:07Z)
• Releasing Graph Neural Networks with Differential Privacy Guarantees [0.81308403220442]
  We propose PrivGNN, a privacy-preserving framework for releasing GNN models in a centralized setting. PrivGNN combines the knowledge-distillation framework with the two noise mechanisms, random subsampling, and noisy labeling, to ensure rigorous privacy guarantees.
  arXiv  Detail & Related papers  (2021-09-18T11:35:19Z)
• Robustness Threats of Differential Privacy [70.818129585404]
  We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions. We study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect the robustness of the model.
  arXiv  Detail & Related papers  (2020-12-14T18:59:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.