Privacy and security have rapidly emerged as first order design constraints. Users now
demand more protection over who can see their data (confidentiality) as well as how it is …

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 (HE) is a privacy-preserving computation technique that enables
computation on encrypted data. Today, the potential of HE remains largely unrealized as it is …

Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the
context of applications like cloud computing, ZKPs can be used by a client (called the …

As security and privacy continue to grow in importance, new techniques, including fully
homomorphic encryption (FHE) and post-quantum cryptography (PQC), have emerged to …

Fully Homomorphic Encryption (FHE) allows various platforms to manipulate encrypted data
offering ideal privacy protection. However, implementing FHE requires significant computing …

Fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs) are emerging as
solutions for data security in distributed environments. However, the widespread adoption of …

In this paper we show how fully homomorphic encryption (FHE) can be accelerated using a
systolic architecture. We begin by analyzing FHE algorithms and then develop systolic or …

Recently, there has been increased emphasis on privacy-preserving computation
technologies such as homomorphic encryption (HE) and Zero-knowledge proof (ZKP) …

Fully homomorphic encryption (FHE) offers the ability to perform computations directly on
encrypted data by encoding numerical vectors onto mathematical structures. However, the …