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
AN - SCOPUS:84923240815
SN - 0386-4812
VL - 65
SP - 78
EP - 88
JO - Journal of Japan Industrial Management Association
JF - Journal of Japan Industrial Management Association
IS - 2
ER -