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

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 …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Despite the rapid increase of data available to train machine-learning algorithms in many
domains, several applications suffer from a paucity of representative and diverse data. The …

We study the membership inference (MI) attack against classifiers, where the attacker's goal
is to determine whether a data instance was used for training the classifier. Through …

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 …

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 …

A surprising phenomenon in modern machine learning is the ability of a highly
overparameterized model to generalize well (small error on the test data) even when it is …

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 …

Membership Inference Attacks (MIAs) can be conducted based on specific
settings/assumptions and experience different limitations. In this paper, first, we provide a …