Membership Inference Attacks (MIAs) aim to determine the presence of a record in a machine learning model's training data by querying the model. Recent work has …
The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. However, existing attacks mostly remain impractical due to …
Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet …
Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target …
Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However …
Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service …
N Carlini, S Chien, M Nasr, S Song… - … IEEE Symposium on …, 2022 - ieeexplore.ieee.org
A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training …
L Liu, Y Wang, G Liu, K Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to various attacks. In this article, we concentrate on membership inference attacks and propose …
J Li, N Li, B Ribeiro - Proceedings of the Eleventh ACM Conference on …, 2021 - dl.acm.org
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 …