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 …

Fast and reliable modeling of distributed data with sensitive information is a major goal of
privacy-preserving machine learning (PPML). Artificial Neural Networks (ANNs) are powerful …

Human activity recognition (HAR) through gait analysis is a very promising research area for
early detection of neurodegenerative diseases because gait abnormalities are typical …

Machine learning has become ubiquitous technology and is being used in many aspects of
our daily lives. However, as machine learning works with a huge amount of data, many …

AI foundation models have recently demonstrated impressive capabilities across a wide
range of tasks. Fine-tuning (FT) is a method of customizing a pre-trained AI foundation …

支持单指令多数据操作的同态加密方案能有效提高密文计算的均摊效率, 但密文结构导致矩阵
运算复杂度高. 在许多应用中, 采用明文− 密文矩阵操作可以在确保安全的同时实现隐私计算 …

Homomorphic encryption (HE) is a promising method in privacy-preserving cloud
computing. Applying HE on feedforwad neural networks has been frequently reported …

Abstract Fully Homomorphic Encryption (FHE) enables computation on encrypted data and
can be used to provide privacy-preserving computation for machine learning models …

Homomorphic encryption (HE) is a promising approach to preserving the privacy of data
used in machine learning by allowing computations to be performed on ciphertext and …

Those homomorphic encryption schemes supporting single instruction multiple data (SIMD)
operations effectively enhance the amortized efficiency of ciphertext computations, yet the …