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 …

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 (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 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 …

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 …

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 …

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