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.

Related papers

• PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies [2.516577526761521]
  We present a novel algorithm PP-GWAS designed to improve upon existing standards in terms of computational efficiency and scalability without sacrificing data privacy. Experimental evaluation with real world and synthetic data indicates that PP-GWAS can achieve computational speeds twice as fast as similar state-of-the-art algorithms. We have assessed its performance using various datasets, emphasizing its potential in facilitating more efficient and private genomic analyses.
  arXiv  Detail & Related papers  (2024-10-10T17:07:57Z)
• Lightweight Cryptanalysis of IoT Encryption Algorithms : Is Quota Sampling the Answer? [0.0]
  Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. It is necessary to test these algorithms for resilience against differential cryptanalysis attacks. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis.
  arXiv  Detail & Related papers  (2024-04-12T00:08:39Z)
• Machine Learning Training Optimization using the Barycentric Correction Procedure [0.0]
  This study proposes combining machine learning algorithms with an efficient methodology known as the barycentric correction procedure (BCP) It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases.
  arXiv  Detail & Related papers  (2024-03-01T13:56:36Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Randomized Dimension Reduction with Statistical Guarantees [0.27195102129095]
  This thesis explores some of such algorithms for fast execution and efficient data utilization. We focus on learning algorithms with various incorporations of data augmentation that improve generalization and distributional provably. Specifically, Chapter 4 presents a sample complexity analysis for data augmentation consistency regularization.
  arXiv  Detail & Related papers  (2023-10-03T02:01:39Z)
• Improved Privacy-Preserving PCA Using Optimized Homomorphic Matrix Multiplication [0.0]
  Principal Component Analysis (PCA) is a pivotal technique widely utilized in the realms of machine learning and data analysis. In recent years, there have been endeavors to utilize homomorphic encryption in privacy-preserving PCA algorithms for the secure cloud computing scenario. We propose a novel approach to privacy-preserving PCA that addresses these limitations, resulting in superior efficiency, accuracy, and scalability compared to previous approaches.
  arXiv  Detail & Related papers  (2023-05-27T02:51:20Z)
• Efficient Dataset Distillation Using Random Feature Approximation [109.07737733329019]
  We propose a novel algorithm that uses a random feature approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel. Our algorithm provides at least a 100-fold speedup over KIP and can run on a single GPU. Our new method, termed an RFA Distillation (RFAD), performs competitively with KIP and other dataset condensation algorithms in accuracy over a range of large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-21T15:56:13Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• New Oracle-Efficient Algorithms for Private Synthetic Data Release [52.33506193761153]
  We present three new algorithms for constructing differentially private synthetic data. The algorithms satisfy differential privacy even in the worst case. Compared to the state-of-the-art method High-Dimensional Matrix Mechanism citeMcKennaMHM18, our algorithms provide better accuracy in the large workload.
  arXiv  Detail & Related papers  (2020-07-10T15:46:05Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.