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 language English 78-88 11 Journal of Japan Industrial Management Association 65 2 Published - 2014

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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

In: Journal of Japan Industrial Management Association, Vol. 65, No. 2, 2014, p. 78-88.

Research output: Contribution to journalArticle

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