Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to infer whether an input …

The vulnerability of machine learning models to membership inference attacks has received
much attention in recent years. However, existing attacks mostly remain impractical due to …

Membership Inference Attacks (MIAs) aim to determine the presence of a record in a
machine learning model's training data by querying the model. Recent work has …

Machine learning (ML) models have been shown to be vulnerable to Membership Inference
Attacks (MIA), which infer the membership of a given data point in the target dataset by …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Machine learning models are vulnerable to membership inference attacks in which an
adversary aims to predict whether or not a particular sample was contained in the target …

Membership inference attacks seek to infer membership of individual training instances of a
model to which an adversary has black-box access through a machine learning-as-a-service …

Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …

Machine learning (ML) models are vulnerable to membership inference attacks (MIAs),
which determine whether a given input is used for training the target model. While there …

Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …