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Ziheng Chen, Jiali Cheng, Hadi Amiri, Kaushiki Nag, Lu Lin, Xiangguo Sun, Gabriele Tolomei
Outline
This paper proposes a novel framework to address the fairness issue in graph-based machine unlearning, which is becoming increasingly important due to strengthened privacy regulations. We address the problem that existing methods can compromise fairness by indiscriminately modifying nodes and edges. We propose a method to achieve fair unlearning by simultaneously optimizing both the graph structure and the model. This involves restructuring the graph by removing unnecessary edges and adding specific edges while maintaining fairness. Furthermore, we introduce a worst-case evaluation mechanism to assess robustness in challenging scenarios. Experimental results using real-world datasets demonstrate that the proposed method achieves more effective and fair unlearning than existing methods.
Takeaways, Limitations
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Takeaways:
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A novel framework for addressing fairness issues in graph-based machine learning.
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Achieving Effective and Fair Unlearning through Graph Reconstruction
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Robustness assessment possible through worst-case evaluation mechanisms
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Validation of effectiveness through experimental results using actual datasets
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Limitations:
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Lack of analysis of the computational complexity of the proposed framework.
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Further research is needed on generalizability to various types of graph data.
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In the worst case, there is a lack of discussion about the limitations of the evaluation mechanism and ways to improve it.
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Lack of consideration of the reliance on a specific fairness metric and the applicability of other metrics.
