To further enhance the reliability of Machine Learning (ML) systems, considerable efforts
have been dedicated to developing privacy protection techniques. Recently, membership …

In Member Inference (MI) attacks, the adversary try to determine whether an instance is used
to train a machine learning (ML) model. MI attacks are a major privacy concern when using …

Machine learning algorithms, when applied to sensitive data, can pose severe threats to
privacy. A growing body of prior work has demonstrated that membership inference attack …

Data used to train machine learning (ML) models can be sensitive. Membership inference
attacks (MIAs), attempting to determine whether a particular data record was used to train an …

Membership inference (MI) attack is currently the most popular test for measuring privacy
leakage in machine learning models. Given a machine learning model, a data point and …

Membership inference (MI) attacks threaten user privacy through determining if a given data
example has been used to train a target model. Existing MI defenses protect the …

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 …

Recent studies propose membership inference (MI) attacks on deep models, where the goal
is to infer if a sample has been used in the training process. Despite their apparent success …

Machine learning (ML) has been widely adopted in various privacy-critical applications, eg,
face recognition and medical image analysis. However, recent research has shown that ML …

Training machine learning models on privacy-sensitive data has become a popular practice,
driving innovation in ever-expanding fields. This has opened the door to new attacks that …