In many applications, data mining has to be done in distributed data scenarios. In such
situations, data owners may be concerned with the misuse of data, hence, they do not want …

We live in a society that relies more and more on the availability of data to make knowledge-
based decisions (Livraga, 2015). The benefits that can be driven by data sharing and …

Privacy is crucial in many applications of machine learning. Legal, ethical and societal
issues restrict the sharing of sensitive data, making it difficult to learn from data sets that are …

Data mining is a process of extracting useful knowledge from large data sets. The typical
process of data collection and data dissemination result in a possible risk of privacy threats …

The advent of Internet of Medical Things (IoMT) has led to a massive revolution in simplifying
patient disease monitoring management approaches, improving diagnosis and treatment …

This paper presents a software-based self-testing methodology for delay fault testing. Delay
faults affect the circuit functionality only when it can be activated in functional mode. A …

In recent years, an increasing amount of data is collected in different and often, not
cooperative, databases. The problem of privacy-preserving, distributed calculations over …

Analyzing and processing data that are siloed and dispersed among multiple distrustful
stakeholders is difficult and can even become impossible when the data are sensitive or …

Abstract Machine learning aims to learn patterns from data. When the data are about people,
a machine learning model will learn information about people. Such models can be used by …

In modern business world, very often two companies collaborate with each other for their
mutual benefit in such a way that, the one starts a transaction and processes a part of it, then …