Abstract Membership Inference Attacks (MIAs) have been considered as one of the major
privacy threats in recent years, especially in machine learning models. Most canonical MIAs …

How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

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 …

The integration of machine learning (ML) in numerous critical applications introduces a
range of privacy concerns for individuals who provide their datasets for model training. One …

Membership Inference Attacks (MIAs) aim to identify specific data samples within the private
training dataset of machine learning models, leading to serious privacy violations and other …

In this evolving era of machine learning security, membership inference attacks have
emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries …

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 …

Membership inference attacks aim to detect if a particular data point was used in training a
model. We design a novel statistical test to perform robust membership inference attacks …

Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …

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 …