The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Speech recordings are a rich source of personal, sensitive data that can be used to support
a plethora of diverse applications, from health profiling to biometric recognition. It is therefore …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Cross-silo federated learning (FL) enables organizations (eg, financial, or medical) to
collaboratively train a machine learning model by aggregating local gradient updates from …

In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …

Federated learning, ie, a mobile edge computing framework for deep learning, is a recent
advance in privacy-preserving machine learning, where the model is trained in a …

Collaborative machine learning and related techniques such as federated learning allow
multiple participants, each with his own training dataset, to build a joint model by training …

Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

Federated learning (FL) is a popular distributed learning framework that can reduce privacy
risks by not explicitly sharing private data. However, recent works have demonstrated that …

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