Speed-up of Data Analysis with Kernel Trick in Encrypted Domain
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929386546003968 |
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| author | Yoo, Joon Soo Song, Baek Kyung Ahn, Tae Min Heo, Ji Won Yoon, Ji Won |
| author_facet | Yoo, Joon Soo Song, Baek Kyung Ahn, Tae Min Heo, Ji Won Yoon, Ji Won |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09716 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speed-up of Data Analysis with Kernel Trick in Encrypted Domain Yoo, Joon Soo Song, Baek Kyung Ahn, Tae Min Heo, Ji Won Yoon, Ji Won Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Speed-up of Data Analysis with Kernel Trick in Encrypted Domain |
| topic | Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2406.09716 |