Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with
substantial potential for enhancing patient care. This paper critically examines this …

Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens.
However, the massive data generated in these cities also poses significant privacy risks …

Synthetic data is often presented as a method for sharing sensitive information in a privacy-
preserving manner by reproducing the global statistical properties of the original data …

The internet has had a dramatic effect on the healthcare industry, allowing documents to be
saved, shared, and managed digitally. This has made it easier to locate and share important …

In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an
aberration from normal behavior, or an unexpected observation requiring immediate …

Edge storage is driven by the emerging edge computing and application intelligence, which
makes the data anonymization become essential to guarantee the security of the data. The …

Introduction: Utilizing medical health data for secondary purposes such as research is
paramount for the development of better pharmaceuticals for patients and improving the …

An enormous volume of data is generated in the Internet of Things (IoT), which needs to be
anonymized before sharing with public or third parties to minimize reidentification risk and …

Graph neural networks (GNNs) have shown promising results on real-life datasets and
applications, including healthcare, finance, and education. However, recent studies have …

Abstract “Garbage in, garbage out” summarises well the importance of high-quality data in
machine learning and artificial intelligence. All data used to train and validate models should …