There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems
utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It …

Fully homomorphic encryption (FHE) is an emerging cryptographic technology that
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …

Due to the increasing popularity of application scenarios such as cloud computing, and the
growing concern of users about the security and privacy of their data, information security …

Purpose The objective of this research is to explore the applicability of machine learning and
fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on …

Homomorphic encryption enables secure cloud computing over the complete data lifecycle.
As so-called in-use encryption methodology, it allows using encrypted data for, eg, data …

Adopting cryptography has given rise to a significant? evolution in AI. This chapter studies
the path and stages of this evolution. We start with reviewing existing relevant surveys …

Security (and Audit) log collection and storage is a crucial process for enterprises around the
globe. Log analysis helps identify potential security breaches and, in some cases, is …

Abstract Machine Learning (ML) has recently shown tremendous success in modeling
various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient …

Security log collection and storage are essential for organizations worldwide. Log analysis
can help recognize probable security breaches and is often required by law. However, many …