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

Joon Soo Yoo; Baek Kyung Song; Tae Min Ahn; Jiwon Heo; Ji Won Yoon

doi:10.1145/3672608.3707713

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

Joon Soo Yoo
, Baek Kyung Song
, Tae Min Ahn
, Jiwon Heo
, Ji Won Yoon^*

^*Corresponding author for this work

School of Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Fully Homomorphic Encryption (FHE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in FHE, 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 FHE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying FHE mechanisms and complementing existing optimizations, notably reduces costly FHE 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.

Original language	English
Title of host publication	40th Annual ACM Symposium on Applied Computing, SAC 2025
Publisher	Association for Computing Machinery
Pages	1055-1064
Number of pages	10
ISBN (Electronic)	9798400706295
DOIs	https://doi.org/10.1145/3672608.3707713
Publication status	Published - 2025 May 14
Event	40th Annual ACM Symposium on Applied Computing, SAC 2025 - Catania, Italy Duration: 2025 Mar 31 → 2025 Apr 4

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	40th Annual ACM Symposium on Applied Computing, SAC 2025
Country/Territory	Italy
City	Catania
Period	25/3/31 → 25/4/4

Bibliographical note

Publisher Copyright:
Copyright © 2025 held by the owner/author(s).

Keywords

fully homomorphic encryption
high-dimensional data analysis
kernel method
privacy-preserving machine learning

ASJC Scopus subject areas

Software

Access to Document

10.1145/3672608.3707713

Cite this

Speed-up of Data Analysis with Kernel Trick in Encrypted Domain. / Yoo, Joon Soo; Song, Baek Kyung; Ahn, Tae Min et al.
40th Annual ACM Symposium on Applied Computing, SAC 2025. Association for Computing Machinery, 2025. p. 1055-1064 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoo, JS, Song, BK, Ahn, TM, Heo, J & Yoon, JW 2025, Speed-up of Data Analysis with Kernel Trick in Encrypted Domain. in 40th Annual ACM Symposium on Applied Computing, SAC 2025. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, pp. 1055-1064, 40th Annual ACM Symposium on Applied Computing, SAC 2025, Catania, Italy, 25/3/31. https://doi.org/10.1145/3672608.3707713

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abstract = "Fully Homomorphic Encryption (FHE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in FHE, 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 FHE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying FHE mechanisms and complementing existing optimizations, notably reduces costly FHE 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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month = may,

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AU - Yoon, Ji Won

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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