Federated Learning: Exact Batch Reconstruction in Honest-but-Curious Setting
Exact batch reconstruction is possible in the honest-but-curious setting for federated learning.
Federated Learning: Exact Batch Reconstruction in Honest-but-Curious Setting
Exact batch reconstruction is possible in federated learning, challenging prior assumptions and highlighting privacy risks.
Gradient Inversion Attacks on Federated Learning: Reducing Reliance on Impractical Auxiliary Datasets
Gradient inversion attacks can accurately recover private training data from shared gradients in Federated Learning, but existing methods heavily rely on impractical assumptions to access excessive auxiliary data. This study proposes a novel method, Gradient Inversion using Practical Image Prior (GI-PIP), that significantly alleviates the auxiliary data requirement on both amount and distribution, posing a greater threat to real-world Federated Learning.
Recovering Augmented Labels and Input Features from Gradients in Gradient Inversion Attacks
The core message of this work is to propose the first analytical algorithm that can accurately recover augmented labels, such as label smoothing and mixup, as well as the last-layer input features from gradients in gradient inversion attacks, without being limited by the existence of bias terms in the network.
Data Leakage from Partial Transformer Gradients: Exposing Vulnerabilities in Distributed Machine Learning
Even access to gradients from a small fraction of a Transformer model's parameters, such as a single layer or even a single linear component, can lead to the reconstruction of private training data, making distributed learning systems more vulnerable than previously thought.
