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Fairness and/or Privacy on Social Graphs

Bartlomiej Surma, Michael Backes, Yang Zhang

TL;DR

This work tackles the dual challenges of fairness and privacy in Graph Neural Networks by empirically evaluating multiple fairness interventions (e.g., adversarial de-biasing, embedding projection, edge weighting, filtering, fair learning) across two social-graph datasets (NBA and Pokec) using diverse GNN architectures (GCN, GraphSAGE, GAT, GIN). It formalizes fairness via Statistical Parity and Equality of Opportunity and assesses privacy risks through fairness and privacy leakage metrics as well as membership inference attacks. The results reveal dataset- and architecture-dependent trade-offs among fairness, privacy, and accuracy, with Kipf GCN and GraphSAGE showing robustness to debiasing and beneficial effects from combining measures in some cases. The findings offer practical guidance for designing fair and privacy-preserving graph learning pipelines and highlight directions for automated, dynamic, and joint optimization of fairness and privacy objectives.

Abstract

Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness and privacy in GNNs, exploring the impact of various fairness-preserving measures on model performance. We conduct experiments across diverse datasets and evaluate the effectiveness of different fairness interventions. Our analysis considers the trade-offs between fairness, privacy, and accuracy, providing insights into the challenges and opportunities in achieving both fair and private graph learning. The results highlight the importance of carefully selecting and combining fairness-preserving measures based on the specific characteristics of the data and the desired fairness objectives. This study contributes to a deeper understanding of the complex interplay between fairness, privacy, and accuracy in GNNs, paving the way for the development of more robust and ethical graph learning models.

Fairness and/or Privacy on Social Graphs

TL;DR

This work tackles the dual challenges of fairness and privacy in Graph Neural Networks by empirically evaluating multiple fairness interventions (e.g., adversarial de-biasing, embedding projection, edge weighting, filtering, fair learning) across two social-graph datasets (NBA and Pokec) using diverse GNN architectures (GCN, GraphSAGE, GAT, GIN). It formalizes fairness via Statistical Parity and Equality of Opportunity and assesses privacy risks through fairness and privacy leakage metrics as well as membership inference attacks. The results reveal dataset- and architecture-dependent trade-offs among fairness, privacy, and accuracy, with Kipf GCN and GraphSAGE showing robustness to debiasing and beneficial effects from combining measures in some cases. The findings offer practical guidance for designing fair and privacy-preserving graph learning pipelines and highlight directions for automated, dynamic, and joint optimization of fairness and privacy objectives.

Abstract

Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness and privacy in GNNs, exploring the impact of various fairness-preserving measures on model performance. We conduct experiments across diverse datasets and evaluate the effectiveness of different fairness interventions. Our analysis considers the trade-offs between fairness, privacy, and accuracy, providing insights into the challenges and opportunities in achieving both fair and private graph learning. The results highlight the importance of carefully selecting and combining fairness-preserving measures based on the specific characteristics of the data and the desired fairness objectives. This study contributes to a deeper understanding of the complex interplay between fairness, privacy, and accuracy in GNNs, paving the way for the development of more robust and ethical graph learning models.

Paper Structure

This paper contains 33 sections, 2 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Experimental Setup
  3. Dataset
  4. NBA
  5. Pokec
  6. Fairness definitions
  7. Statistical Parity
  8. Equality of Opportunity
  9. Fairness Attribute Leakage
  10. Privacy Definitions
  11. Privacy Attribute Leakage
  12. Membership Inference Attack
  13. Methodology
  14. Graph Convolution Networks
  15. GraphSAGE
  16. ...and 18 more sections

Figures (7)

  • Figure 1: Base model performance on all metrics, averaged with standard deviation for different test / train splits
  • Figure 2: Performance of adversarial de-biasing with different $\alpha$ and $\beta$ parameters. X marks baseline model performance (without fairness countermeasures)
  • Figure 3: Different privacy sensitive attribues leakage under Adversarial debiasing
  • Figure 4: Performance of fairness countermeasures applied to different GCNs on nba dataset
  • Figure 5: Performance of fairness countermeasures applied to different GCNs on Pokec z dataset
  • ...and 2 more figures