Federated Learning (FL) is a distributed learning paradigm that enables different parties to
train a model together for high quality and strong privacy protection. In this scenario …

Abstract Model inversion attacks are a type of privacy attack that reconstructs private data
used to train a machine learning model, solely by accessing the model. Recently, white-box …

Abstract Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from
released models by utilizing output information, raising extensive concerns about the …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …

In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML)
model to infer and reconstruct private training data. Remarkable progress has been made in …

Recent model inversion attack algorithms permit adversaries to reconstruct a neural
network's private and potentially sensitive training data by repeatedly querying the network …

Data privacy is of great concern in cloud machine-learning service platforms when sensitive
data are exposed to service providers. While private computing environments (eg secure …

With the development of deep learning, deep neural network (DNN)-based application have
become an indispensable aspect of daily life. However, recent studies have shown that …

Model Inversion (MI) attacks aim at leveraging the output information of target models to
reconstruct privacy-sensitive training data, raising widespread concerns on privacy threats of …