The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …

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 …

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …

Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

The Internet of Things (IoT) provides the ability for humans and computers to learn and
interact from billions of things that include sensors, actuators, services, and other Internet …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

The wide-spread availability of rich data has fueled the growth of machine learning
applications in numerous domains. However, growth in domains with highly-sensitive data …