Purpose Recently, deep learning (DL) has been widely applied in various aspects of human
endeavors. However, studies have shown that DL models may also be a primary cause of …

Abstract Machine learning (ML) has revolutionized various industries, but concerns about
privacy and security have emerged as significant challenges. Membership inference attacks …

The success of machine learning (ML) depends on the availability of large-scale datasets.
However, recent studies have shown that models trained on such datasets are vulnerable to …

Member Inference Attack (MIA) is a key measure for evaluating privacy leakage in Machine
Learning (ML) models, aiming to distinguish private members from non-members by training …

Machine learning poses severe privacy concerns as it has been shown that the learned
models can reveal sensitive information about their training data. Many works have …

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 …

To further enhance the reliability of Machine Learning (ML) systems, considerable efforts
have been dedicated to developing privacy protection techniques. Recently, membership …

Recent researches demonstrate that deep learning models are vulnerable to membership
inference attacks. Few defenses have been proposed, but suffer from compromising the …

Large-capacity machine learning models are vulnerable to membership inference attacks
that disclose the privacy of the training dataset. The privacy concerns posed by membership …

Over the past few years, a variety of membership inference attacks against deep learning
models have emerged, raising significant privacy concerns. These attacks can easily infer …