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 …

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

The Membership Inference Attack (MIA) is the process of determining whether a sample
comes from the training dataset (in) of a machine learning model or not (out). This attack …

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 …

Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to
membership inference attack (MIA), where adversaries can determine if specific data was …

Membership inference attacks (MIAs) aim to determine whether a specific sample was used
to train a predictive model. Knowing this may indeed lead to a privacy breach. Most MIAs …

A large body of research has shown that machine learning models are vulnerable to
membership inference (MI) attacks that violate the privacy of the participants in the training …

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 …

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 …