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 …

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 …

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

When building machine learning models using sensitive data, organizations should ensure
that the data processed in such systems is adequately protected. For projects involving …

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 …

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 …

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 …