The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system
and the amount of protection offered by privacy-enhancing technologies. In this way, privacy …

The collection and analysis of data are continuously growing due to the pervasiveness of
computing devices. The analysis of such information is fostering businesses and …

How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

In recent years, big data have become a hot research topic. The increasing amount of big
data also increases the chance of breaching the privacy of individuals. Since big data …

Service providers like Google and Amazon are moving into the SaaS (Software as a
Service) business. They turn their huge infrastructure into a cloud-computing environment …

In recent years, privacy-preserving data mining has been studied extensively, because of
the wide proliferation of sensitive information on the internet. A number of algorithmic …

Protecting Cyber-physical Systems (CPSs) is highly important for preserving sensitive
information and detecting cyber threats. Developing a robust privacy-preserving anomaly …

This paper explores the possibility of using multiplicative random projection matrices for
privacy preserving distributed data mining. It specifically considers the problem of computing …

Modern power systems depend on cyber-physical systems to link physical devices and
control technologies. A major concern in the implementation of smart power networks is to …

Advances in computer networking and database technologies have enabled the collection
and storage of vast quantities of data. Data mining can extract valuable knowledge from this …