While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

This paper presents a comprehensive systematic review of generative models (GANs, VAEs,
DMs, and LLMs) used to synthesize various medical data types, including imaging …

The sampling problem under local differential privacy has recently been studied with
potential applications to generative models, but a fundamental analysis of its privacy-utility …

Generally, the small size of public medical imaging datasets coupled with stringent privacy
concerns, hampers the advancement of data-hungry deep learning models in medical …

The proliferation of media-sharing platforms has led to issues with illegally edited content
and the distribution of pornography. To protect personal information, de-identification …

To release medical images that can be freely used in downstream processes while
maintaining their utility, it is necessary to remove personal features from the images while …

Intelligent and connected vehicles (ICVs) collect vast video footage, inadvertently capturing
privacy-sensitive data. To address this, we introduce the IDD-ICV dataset, featuring diverse …