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 …

Membership Inference attacks (MIAs) aim to predict whether a data sample was present in
the training data of a machine learning model or not, and are widely used for assessing the …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

Text-to-image generation models have recently attracted unprecedented attention as they
unlatch imaginative applications in all areas of life. However, developing such models …

Recent years have witnessed the tremendous success of diffusion models in data synthesis.
However, when diffusion models are applied to sensitive data, they also give rise to severe …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Abstract Machine unlearning (MU) seeks to remove knowledge of specific data samples
from trained models without the necessity for complete retraining, a task made challenging …

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Membership Inference Attacks (MIA) aim to infer whether a target data record has been
utilized for model training or not. Prior attempts have quantified the privacy risks of language …

Explainability is increasingly recognized as an enabling technology for the broader adoption
of machine learning (ML), particularly for safety-critical applications. This has given rise to …