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

arXiv: https://arxiv.org/abs/2406.09716 · HTML: https://mlsanity.com/paper/2406.09716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Low Latency Privacy Preserving Inference](https://mlsanity.com/paper/1812.10659/) (2018)
- [nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data](https://mlsanity.com/paper/1908.04172/) (2019)
- [On Lattices, Learning with Errors, Random Linear Codes, and Cryptography](https://mlsanity.com/paper/2401.03703/) (2024)
- [HEAAN Demystified: Accelerating Fully Homomorphic Encryption Through Architecture-centric Analysis and Optimization](https://mlsanity.com/paper/2003.04510/) (2020)
- [A General Purpose Transpiler for Fully Homomorphic Encryption](https://mlsanity.com/paper/2106.07893/) (2021)
- [S-FaaS: Trustworthy and Accountable Function-as-a-Service using Intel SGX](https://mlsanity.com/paper/1810.06080/) (2018)

## Abstract (arXiv)

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.