Membership inference attacks (MIAs) aim to determine whether a specific sample was used
to train a predictive model. Knowing this may indeed lead to a privacy breach. Most MIAs …

Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to
membership inference attack (MIA), where adversaries can determine if specific data was …

We demonstrate how a target model's generalization gap leads directly to an effective
deterministic black box membership inference attack (MIA). This provides an upper bound …

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 …

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 …

Membership inference attacks (MIAs) against machine learning systems have drawn
tremendous attention from information security researchers. By MIA, an adversary can …

With the popularity of machine learning, it has been a growing concern on the trained model
revealing the private information of the training data. Membership inference attack (MIA) …

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 …

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