The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent
energy management, and distributed energy integration poses new requirements for …

Cardiovascular disease (CVD) is a life‐threatening disease rising considerably in the world.
Early detection and prediction of CVD as well as other heart diseases might protect many …

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 …

Random forests have been one successful ensemble algorithms in machine learning.
Various techniques have been utilized to preserve the privacy of random forests from …

Decision trees are among the most widespread machine learning models used for data
classification, in particular due to their interpretability that makes it easy to explain their …

Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy
of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic …

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 …

Homomorphic encryption (HE) protects data in-use, but can be computationally expensive.
To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have …

In recent years, deep learning has become an increasingly popular approach to modelling
data due to its ability to detect abstract underlying patterns in data. Its practical applications …

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 …