Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

As a long-term threat to the privacy of training data, membership inference attacks (MIAs)
emerge ubiquitously in machine learning models. Existing works evidence strong …

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 …

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 …

Deep learning models often raise privacy concerns as they leak information about their
training data. This leakage enables membership inference attacks (MIA) that can identify …

It is observed in the literature that data augmentation can significantly mitigate membership
inference (MI) attack. However, in this work, we challenge this observation by proposing new …

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 …

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 …