Abstract Machine learning classification is used in numerous settings nowadays, such as
medical or genomics predictions, spam detection, face recognition, and financial predictions …

Predicting students' grades has emerged as a major area of investigation in education due
to the desire to identify the underlying factors that influence academic performance. Because …

Sum-product networks (SPNs) are a promising and novel type of probabilistic model, which
has been receiving significant attention in recent years. There are, however, several open …

In this paper, we describe a protocol framework that can perform classification tasks in a
privacy-preserving manner. To demonstrate the feasibility of the proposed framework, we …

In recent years, the amount of Internet information data has exploded, and the problem of
“information overload” has become a huge challenge for Internet development. Through the …

We propose an efficient unconditionally secure protocol for privacy preserving comparison
of ℓ ℓ-bit integers when both integers are shared between two semi-honest parties. Using …

Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-
devices. During such inferences, a low-precision arithmetic representation can enable …

In this paper, we propose a privacy-preserving naive Bayes classifier based on a leveled
homomorphic encryption scheme due to Brakerski-Gentry-Vaikuntanuthan (BGV). The …

Conventional Von Neumann microprocessors are inefficient for supporting machine
intelligence due to layers of abstraction, limiting the feasibility of machine-learning …

Malware detections continue to be a challenging task as attackers may be aware of the rules
used in malware detection mechanisms and constantly generate new breeds of malware to …