The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

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 …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

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 of digital information by governments, corporations, and individuals has
created tremendous opportunities for knowledge-and information-based decision making …

Recent years have witnessed remarkable successes of machine learning in various
applications. However, machine learning models suffer from a potential risk of leaking …

The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to
apply in practice. The parameters of differential privacy have an intuitive theoretical …

Abstract We present Airavat, a MapReduce-based system which provides strong security
and privacy guarantees for distributed computations on sensitive data. Airavat is a novel …