Label Inference Attacks against Federated Unlearning

Federated Unlearning FU has emerged as a promising solution to respond to the right to be forgotten of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU...

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning

Federated Unlearning FU has emerged as a critical compliance mechanism for data privacy regulations, requiring unlearned clients to provide verifiable Proof of Federated Unlearning PoFU to auditors upon data removal requests. However, we uncover a significant privacy vulnerability: when gradient...