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 …

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 (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 …

Membership inference (MI) attacks threaten user privacy through determining if a given data
example has been used to train a target model. However, it has been increasingly …

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 …

Membership inference attacks (MIAs) have powerful attack ability to threaten the privacy of
users. In general, it mainly utilizes model-based or metric-based inference methods to infer …

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

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 …

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 …

Given a machine learning model and a record, membership attacks determine whether this
record was used as part of the model's training dataset. Membership inference can present a …