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 …

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 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 …

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 …

Machine Learning (ML) techniques have been applied to many real-world applications to
perform a wide range of tasks. In practice, ML models are typically deployed as the black …

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 …

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 …

Given a machine learning model and a record, membership attacks determine whether this
record was used as part of the model's training dataset. Membership inference can present a …

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 …

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 …