Membership inference (MI) attack is currently the most popular test for measuring privacy
leakage in machine learning models. Given a machine learning model, a data point and …

Machine learning models can leak private information about their training data, but the
standard methods to measure this risk, based on membership inference attacks (MIAs), have …

Membership inference is one of the simplest privacy threats faced by machine learning
models that are trained on private sensitive data. In this attack, an adversary infers whether a …

Membership inference attacks are a key measure to evaluate privacy leakage in machine
learning (ML) models. It is important to train ML models that have high membership privacy …

Data used to train machine learning (ML) models can be sensitive. Membership inference
attacks (MIAs), attempting to determine whether a particular data record was used to train an …

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 …

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 have been shown to leak information violating the privacy of their
training set. We focus on membership inference attacks on machine learning models which …

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 …

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 …