Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy
protection; nevertheless, object recognition research typically assumes access to complete …

Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …

The large-scale whole-slide images (WSIs) facilitate the learning-based computational
pathology methods. However, the gigapixel size of WSIs makes it hard to train a …

Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …

Federated learning (FL) allows the collaborative training of AI models without needing to
share raw data. This capability makes it especially interesting for healthcare applications …

Privacy preserving machine learning algorithms are crucial for learning models over user
data to protect sensitive information. Motivated by this, differentially private stochastic …

Despite the rapid increase of data available to train machine-learning algorithms in many
domains, several applications suffer from a paucity of representative and diverse data. The …

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 …