Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model. Prior work has shown that the attack is …
Membership Inference Attacks (MIAs) are widely used to evaluate the propensity of a machine learning (ML) model to memorize an individual record and the privacy risk …
Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However …
J Niu, P Liu, X Zhu, K Shen, Y Wang, H Chi… - Journal of Information …, 2024 - Elsevier
Membership inference (MI) attacks mainly aim to infer whether a data record was used to train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
Membership Inference Attacks (MIAs) can be conducted based on specific settings/assumptions and experience different limitations. In this paper, first, we provide a …
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 …
Machine learning (ML) models have been shown to be vulnerable to Membership Inference Attacks (MIA), which infer the membership of a given data point in the target dataset by …
N Senavirathne, V Torra - … 13th International Symposium, CSS 2021, Virtual …, 2022 - Springer
Membership inference attacks (MIA) have been identified as a distinct threat to privacy when sensitive personal data are used to train the machine learning (ML) models. This work is …
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 …