Yilong Wang
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126
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2026
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
This paper introduces 'unlearning corruption attacks,' demonstrating that the performance degradation inherent in approximate graph unlearning can be exploited by an adversary to significantly reduce the accuracy of Graph Neural Networks (GNNs) after targeted data deletion.
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