Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

A crucial module of the widely applied machine learning (ML) model is the model training
phase, which involves large-scale training data, often including sensitive private data. ML …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

Models can expose sensitive information about their training data. In an attribute inference
attack, an adversary has partial knowledge of some training records and access to a model …

The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning—a
collaborative machine learning framework aimed at addressing the main limitations of …

Machine learning models are established with a variety of data collected from individual
users who are concerned about their privacy. Various cloud service providers (eg, Amazon …

Machine Learning (ML) has emerged as a core technology to provide learning models to
perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of …

Machine learning (ML) models have been deployed for high-stakes applications. Due to
class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on …

In the last decade, machine learning has been widely adopted in many areas and the trend
not only continues but accelerates. This has raised many issues ranging from ethics and …