Z Ying, Y Zhang, X Liu - Proceedings of the 2020 Workshop on Privacy …, 2020 - dl.acm.org
The membership inference attack refers to the attacker's purpose to infer whether the data sample is in the target classifier training dataset. The ability of an adversary to ascertain the …
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 …
Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's …
D Chen, N Yu, M Fritz - arXiv preprint arXiv:2207.05801, 2022 - arxiv.org
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 …
Machine learning models leak significant amount of information about their training sets, through their predictions. This is a serious privacy concern for the users of machine learning …
In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the target …
While being deployed in many critical applications as core components, machine learning (ML) models are vulnerable to various security and privacy attacks. One major privacy attack …
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 …
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 …