Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing

TL;DR

This paper introduces FGU, a novel method for fair graph unlearning that preserves privacy, maintains accuracy, and reduces bias by employing shard models and bi-level debiasing techniques.

Contribution

The paper presents FGU, a new graph unlearning approach that incorporates fairness regularization and global alignment to mitigate bias and enhance fairness in unlearned models.

Findings

FGU achieves superior fairness compared to baseline methods.

FGU maintains high accuracy and privacy standards.

FGU is robust across diverse unlearning scenarios.

Abstract

Graph unlearning is a crucial approach for protecting user privacy by erasing the influence of user data on trained graph models. Recent developments in graph unlearning methods have primarily focused on maintaining model prediction performance while removing user information. However, we have observed that when user information is deleted from the model, the prediction distribution across different sensitive groups often changes. Furthermore, graph models are shown to be prone to amplifying biases, making the study of fairness in graph unlearning particularly important. This raises the question: Does graph unlearning actually introduce bias? Our findings indicate that the predictions of post-unlearning models become highly correlated with sensitive attributes, confirming the introduction of bias in the graph unlearning process. To address this issue, we propose a fair graph unlearning method, FGU. To guarantee privacy, FGU trains shard models on partitioned subgraphs, unlearns the requested data from the corresponding subgraphs, and retrains the shard models on the modified subgraphs. To ensure fairness, FGU employs a bi-level debiasing process: it first enables shard-level fairness by incorporating a fairness regularizer in the shard model retraining, and then achieves global-level fairness by aligning all shard models to minimize global disparity. Our experiments demonstrate that FGU achieves superior fairness while maintaining privacy and accuracy. Additionally, FGU is robust to diverse unlearning requests, ensuring fairness and utility performance across various data distributions.