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

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 …

Machine learning (ML) has become a core component of many real-world applications and
training data is a key factor that drives current progress. This huge success has led Internet …

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 …

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 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 …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

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 …

We study the membership inference (MI) attack against classifiers, where the attacker's goal
is to determine whether a data instance was used for training the classifier. Through …