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 …

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

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 …

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 attacks involve reconstructing the training data of a target model, which
raises serious privacy concerns for machine learning models. However, these attacks …

Neural networks are susceptible to data inference attacks such as the membership inference
attack, the adversarial model inversion attack and the attribute inference attack, where the …

Model Inversion (MI) attacks aim to disclose private information about the training data by
abusing access to the pre-trained models. These attacks enable adversaries to reconstruct …

Abstract Model Inversion (MI) attacks aim to reconstruct private training data by abusing
access to machine learning models. Contemporary MI attacks have achieved impressive …