As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

AI has provided us with the ability to automate tasks, extract information from vast amounts of
data, and synthesize media that is nearly indistinguishable from the real thing. However …

This paper studies model-inversion attacks, in which the access to a model is abused to infer
information about the training data. Since its first introduction by [??], such attacks have …

Given the ubiquity of deep neural networks, it is important that these models do not reveal
information about sensitive data that they have been trained on. In model inversion attacks …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

The wide application of deep learning technique has raised new security concerns about the
training data and test data. In this work, we investigate the model inversion problem under …

Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …

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 …

In a model inversion attack, an adversary attempts to reconstruct the training data records of
a target model using only the model's output. In launching a contemporary model inversion …

In order to achieve good performance and generalisability, medical image segmentation
models should be trained on sizeable datasets with sufficient variability. Due to ethics and …