Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine
learning (PPML). Several PPML models have been proposed based on various FHE …

Recently, the standard ResNet-20 network was successfully implemented on the fully
homomorphic encryption scheme, residue number system variant Cheon-Kim-Kim-Song …

Inference of machine learning models with data privacy guarantees has been widely studied
as privacy concerns are getting growing attention from the community. Among others, secure …

Outsourced computation for neural networks allows users access to state-of-the-art models
without investing in specialized hardware and know-how. The problem is that the users lose …

Secure transformer inference has emerged as a prominent research topic following the
proliferation of ChatGPT. Existing solutions are typically interactive, involving substantial …

Homomorphic encryption (HE) is one of the representative solutions to privacy-preserving
machine learning (PPML) classification enabling the server to classify private data of clients …

Due to the increasing popularity of application scenarios such as cloud computing, and the
growing concern of users about the security and privacy of their data, information security …

In the era of cloud computing and machine learning, data has become a highly valuable
resource. Recent history has shown that the benefits brought forth by this data driven culture …

By supporting computation on encrypted data, fully homomorphic encryption (FHE) offers the
potential for privacy-preserving computation offloading. However, its applicability is …

The sign function can be adopted to implement the comparison operation, max function, and
rectified linear unit (ReLU) function in the Cheon–Kim–Kim–Song (CKKS) scheme; hence …