Nowadays, machine learning is widely used in various applications. However, machine
learning models are vulnerable to various membership inference attacks (MIAs) that leak …

Membership Inference Attack (MIA) determines the presence of a record in a machine
learning model's training data by querying the model. Prior work has shown that the attack is …

Membership Inference Attacks (MIAs) are widely used to evaluate the propensity of a
machine learning (ML) model to memorize an individual record and the privacy risk …

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

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 …

Membership Inference Attacks (MIAs) can be conducted based on specific
settings/assumptions and experience different limitations. In this paper, first, we provide a …

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 …

Membership inference attacks (MIA) have been identified as a distinct threat to privacy when
sensitive personal data are used to train the machine learning (ML) models. This work is …

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to
various attacks. In this article, we concentrate on membership inference attacks and propose …