  Homomorphic encryption (HE) is pivotal for secure computation on encrypted
data, crucial in privacy-preserving data analysis. However, efficiently
processing high-dimensional data in HE, especially for machine learning and
statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present
an effective acceleration method using the kernel method for HE schemes,
enhancing time performance in ML/STAT algorithms within encrypted domains. This
technique, independent of underlying HE mechanisms and complementing existing
optimizations, notably reduces costly HE multiplications, offering near
constant time complexity relative to data dimension. Aimed at accessibility,
this method is tailored for data scientists and developers with limited
cryptography background, facilitating advanced data analysis in secure
environments.
