Privacy preserving distributed data mining based on secure multi-party computation

Document Type

Article

Publication Date

3-1-2020

Abstract

Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining self-owned data of a party are rather mature. However, how to perform distributed data mining to obtain information from data owned by multiple parties without privacy leakage remains a big challenge. While secure multi-party computation (MPC) may potentially address this challenge, several issues have to be overcome for practical realizations. In this paper, we point out two unsupported tasks of MPC that are common in the real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results show that our design and implementation are feasible and effective for privacy preserving distributed data mining.

Identifier

85079093057 (Scopus)

Publication Title

Computer Communications

External Full Text Location

https://doi.org/10.1016/j.comcom.2020.02.014

e-ISSN

1873703X

ISSN

01403664

First Page

208

Last Page

216

Volume

153

Fund Ref

Science and Technology Commission of Shanghai Municipality

This document is currently not available here.

Share

COinS