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 …

Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair
machine learning aims at minimizing discrimination against protected groups by, for …

In cross-device federated learning (FL) setting, clients such as mobiles cooperate with the
server to train a global machine learning model, while maintaining their data locally …

In Federated learning, models in training will unintentionally memorize detailed information
about private data, and the aggregation process on the central server requires users to …

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …

Differential privacy (DP) has been applied in deep learning for preserving privacy of the
underlying training sets. Existing DP practice falls into three categories—objective …

We analyze various factors affecting the proper functioning of MIA and MINT two research
lines aimed at detecting data used for training. The difference between these lines lies in the …

Membership inference attacks aim to infer whether a data record has been used to train a
target model by observing its predictions. In sensitive domains such as healthcare, this can …

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 …