As machine learning as a service continues gaining popularity, concerns about privacy and
intellectual property arise. Users often hesitate to disclose their private information to obtain …

This paper introduces a new method for training decision trees and random forests using
CKKS homomorphic encryption (HE) in cloud environments, enhancing data privacy from …

Privacy-preserving decision tree evaluation (PDTE) allows a client that holds feature vectors
to perform inferences against a decision tree model on the server side without revealing …

A decision tree is a common algorithm in machine learning, which performs classification
prediction based on a tree structure. In real-world scenarios, input attribute values may be …

This paper introduces a novel adaptation of counting sort that enables sorting of encrypted
data using Fully Homomorphic Encryption (FHE). Our approach represents the first known …

In the ever-evolving landscape of Information Technologies, private decentralized
computing on an honest yet curious server has emerged as a prominent paradigm. While …

Private comparison schemes constructed on homomorphic encryption offer the
noninteractive and parallelizable features, and have advantages in communication …

Abstract {\em Range searching} is the problem of preprocessing a set of points $ P $, such
that given a query range $\gamma $ we can efficiently compute some function $ f …

Rule-based learning involves using specific rules to categorize or identify datasets. This
study introduces a new approach called homomorphic encryption-based rule induction …

In recent years, machine learning (ML) has become increasingly popular in various fields of
activity. Cloud platforms have also grown in popularity, as they offer services that are more …