Smart cities are expected to improve the quality of daily life, promote sustainable
development, and improve the functionality of urban systems. Now that many smart systems …

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 problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …

This article summarizes and draws connections among diverse streams of theoretical and
empirical research on the economics of privacy. We focus on the economic value and …

In this work, we investigate whether privacy and fairness can be simultaneously achieved by
a single classifier in several different models. Some of the earliest work on fairness in …

Federated learning (FL) is an emerging distributed learning paradigm with default client
privacy because clients can keep sensitive data on their devices and only share local …

Many resource allocation problems can be formulated as an optimization problem whose
constraints contain sensitive information about participating users. This paper concerns a …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …