Model Inversion Attack Against Federated Unlearning

Summary

This paper presents FUIA, a federated unlearning inversion attack that exposes privacy leakage in federated unlearning (FU), the method used to remove specific data's influence from federated learning models. The first systematic study of FU privacy vulnerabilities, it applies to sample, client and class unlearning. An honest-but-curious server records parameter changes during unlearning, infers gradients of forgotten data, and reconstructs its features or labels across multiple benchmark datasets and FU methods.

Mitigation

The paper explores two potential defense strategies that trade off privacy protection against model performance. No specific defense details are given in the source text.

Topics

• Training data and privacy

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