Membership inference (MI) attacks threaten user privacy through determining if a given data
example has been used to train a target model. However, it has been increasingly …

Graph Neural Networks (GNNs) have been widely used in various graph-based
applications. Recent studies have shown that GNNs are vulnerable to link-level membership …

摘要机器学习被广泛应用于各个领域, 已成为推动各行业革命的强大动力,
极大促进了人工智能的繁荣与发展. 同时, 机器学习模型的训练和预测均需要大量数据 …

Recent model inversion attack algorithms permit adversaries to reconstruct a neural
network's private training data just by repeatedly querying the network and inspecting its …

In this paper, we study neural network architectures that are robust to model inversion
attacks. It is well-known that standard network architectures are vulnerable to model …

Membership Inference Attacks (MIAs) infer whether a data point is in the training data of a
machine learning model. It is a threat while being in the training data is private information of …

Machine learning (ML) models are susceptible to membership inference attacks (MIAs),
which aim to infer whether a particular sample was involved in model training. Previous …

Existing works have demonstrated that machine learning models may leak sensitive
information of the training set to adversaries who launch the membership inference attacks …

Purpose. Machine learning (ML) has revolutionized data processing and analysis, with
applications in health showing great promise. However, ML poses privacy risks, as models …

In the digital age, Online Social Networks (OSNs) have emerged as epicenters of human
interaction, facilitating the creation, sharing, and dissemination of information at an …