Speed-up of Data Analysis with Kernel Trick in Encrypted Domain
This work addresses a bottleneck in privacy-preserving data analysis for data scientists and developers with limited cryptography expertise, though it appears incremental as it builds on existing HE optimizations.
The paper tackles the challenge of efficiently processing high-dimensional data in homomorphic encryption for machine learning and statistical algorithms by introducing a kernel method that reduces costly multiplications, achieving near constant time complexity relative to data dimension.
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.