Speed-up of Data Analysis with Kernel Trick in Encrypted Domain

• URL: http://arxiv.org/abs/2406.09716v1
• Date: Fri, 14 Jun 2024 04:49:40 GMT
• Title: Speed-up of Data Analysis with Kernel Trick in Encrypted Domain
• Authors: Joon Soo Yoo, Baek Kyung Song, Tae Min Ahn, Ji Won Heo, Ji Won Yoon,
• Abstract summary: Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. We present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains.
• Score: 2.592307869002029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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