Rising utilization of novel artificial intelligence and other data-driven applications sets a
demand for privacypreserving large-scale data management. In the current, cloud-centric …

For privacy concerns to be addressed adequately in today's machine-learning (ML) systems,
the knowledge gap between the ML and privacy communities must be bridged. This article …

With the rapid development of technology, data is becoming ubiquitous. User privacy and
data security are drawing much attention over the recent years, especially with the European …

When training a machine learning model, it is standard procedure for the researcher to have
full knowledge of both the data and model. However, this engenders a lack of trust between …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

As machine learning models become increasingly ubiquitous, ensuring privacy protection
has emerged as a critical concern. This paper presents an in-depth exploration of privacy …

Nowadays different entities (such as hospitals, cyber security companies, banks, etc.) collect
data of the same nature but often with different statistical properties. It has been shown that if …

Machine learning (ML) tools are among the promising data-driven techniques that can help
solve many real-life problems. However these tools rely on the collection of large volumes of …

As an implementation methodology of the artificial intelligence, machine learning techniques
have reported impressive performance in a variety of application domains, such as risk …

The protection of user privacy is an important concern in machine learning, as evidenced by
the rolling out of the General Data Protection Regulation (GDPR) in the European Union …