Non-interactive cryptographic computing, Fully Homomorphic Encryption (FHE), provides a
promising solution for private neural network training on encrypted data. One challenge of …

Homomorphic encryption is one of the main solutions for building secure and privacy-
preserving solutions for Machine Learning as a Service, a major challenge in a society …

The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data
loads across multiple types of devices taking advantage of the different strengths of each …

Large language models (LLMs) offer personalized responses based on user interactions,
but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a …

Fully homomorphic encryption (FHE) is a cryptographic technology capable of resolving
security and privacy problems in cloud computing by encrypting data in use. However, FHE …

Designing privacy-preserving deep learning models is a major challenge within the deep
learning community. Homomorphic Encryption (HE) has emerged as one of the most …

The remarkable progress of deep learning in dermatological tasks has brought us closer to
achieving diagnostic accuracies comparable to those of human experts. However, while …

The era of digitization and IoT devices is marked by the constant storage of massive
amounts of data. The growing adoption of smart home environments, which use sensors and …

Recent research has explored the implementation of privacy-preserving deep neural
networks solely using fully homomorphic encryption. However, its practicality has been …

This work investigates using Homomorphic Encryption (HE) to assist the security evaluation
of cryptographic devices without revealing side-channel information. For the first time, we …