Privacy-preserving distributed calculation methods of a least-squares estimator for linear regression models

Research output: Contribution to journalArticle

Abstract

In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.

Original languageEnglish
Pages (from-to)78-88
Number of pages11
JournalJournal of Japan Industrial Management Association
Volume65
Issue number2
Publication statusPublished - 2014

Fingerprint

Privacy Preserving
Least Squares Estimator
Linear Regression Model
Linear regression
Regression analysis
Regression Analysis
Privacy
Numerical Experiment
Iteration
Calculate
Linear regression model
Calculation method
Least squares estimator
Privacy preserving
Evaluate
Experiments

Keywords

  • Distributed computation
  • Least-squares method
  • Linear regression model
  • Privacy-preserving data mining

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Applied Mathematics
  • Management Science and Operations Research
  • Strategy and Management

Cite this

@article{d2e3db7de34e4e82abb01c15e37fe06c,
title = "Privacy-preserving distributed calculation methods of a least-squares estimator for linear regression models",
abstract = "In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.",
keywords = "Distributed computation, Least-squares method, Linear regression model, Privacy-preserving data mining",
author = "Tota Suko and Shunsuke Horii and Manabu Kobayashi and Masayuki Goto and Toshiyasu Matsushima and Shigeichi Hirasawa",
year = "2014",
language = "English",
volume = "65",
pages = "78--88",
journal = "Journal of Japan Industrial Management Association",
issn = "0386-4812",
publisher = "Nihon Keikei Kogakkai",
number = "2",

}

TY - JOUR

T1 - Privacy-preserving distributed calculation methods of a least-squares estimator for linear regression models

AU - Suko, Tota

AU - Horii, Shunsuke

AU - Kobayashi, Manabu

AU - Goto, Masayuki

AU - Matsushima, Toshiyasu

AU - Hirasawa, Shigeichi

PY - 2014

Y1 - 2014

N2 - In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.

AB - In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.

KW - Distributed computation

KW - Least-squares method

KW - Linear regression model

KW - Privacy-preserving data mining

UR - http://www.scopus.com/inward/record.url?scp=84923240815&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923240815&partnerID=8YFLogxK

M3 - Article

VL - 65

SP - 78

EP - 88

JO - Journal of Japan Industrial Management Association

JF - Journal of Japan Industrial Management Association

SN - 0386-4812

IS - 2

ER -