Membership inference attacks seek to infer the membership of individual training instances
of a privately trained model. This paper presents a membership privacy analysis and …

The membership inference attack refers to the attacker's purpose to infer whether the data
sample is in the target classifier training dataset. The ability of an adversary to ascertain the …

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 …

Large capacity machine learning (ML) models are prone to membership inference attacks
(MIAs), which aim to infer whether the target sample is a member of the target model's …

As a long-term threat to the privacy of training data, membership inference attacks (MIAs)
emerge ubiquitously in machine learning models. Existing works evidence strong …

Machine learning models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

While being deployed in many critical applications as core components, machine learning
(ML) models are vulnerable to various security and privacy attacks. One major privacy attack …

Membership inference attacks are a key measure to evaluate privacy leakage in machine
learning (ML) models. It is important to train ML models that have high membership privacy …

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 …