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Title: Collaborative classification mechanism for privacy-preserving on horizontally partitioned data
Authors: Zhang, ZC
Chung, FL 
Wang, ST
Keywords: Classification
Collaborative learning
Support vector machine
Issue Date: 2019
Publisher: Taylor & Francis
Source: Automatika, 2019, v. 60, no. 1, p. 58-67 How to cite?
Journal: Automatika 
Abstract: We propose a novel two-party privacy-preserving classification solution called Collaborative Classification Mechanism for Privacy-preserving ((CMP)-M-2 (2))over horizontally partitioned data that is inspired from the fact, that global and local learning can be independently executed in two parties. This model collaboratively trains the decision boundary from two hyper-planes individually constructed by its own privacy data and global data. (CMP2)-M-2 can hide true data entries and ensure the two-parties' privacy. We describe its definition and provide an algorithm to predict future data point based on Goethals's Private Scalar Product Protocol. Moreover, we show that (CMP2)-M-2 can be transformed into existing Minimax Probability Machine (MPM), Support Vector Machine (SVM) and Maxi-Min Margin Machine (M-4) model when privacy data satisfy certain conditions. We also extend (CMP2)-M-2 to a nonlinear classifier by exploiting kernel trick. Furthermore, we perform a series of evaluations on real-world benchmark data sets. Comparison with SVM from the point of protecting privacy demonstrates the advantages of our new model.
ISSN: 0005-1144
EISSN: 1848-3380
DOI: 10.1080/00051144.2019.1578039
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Zhancheng Zhang, Fu-Lai Chung & Shitong Wang (2019) Collaborative classification mechanism for privacy-Preserving on horizontally partitioned data, Automatika, 60:1,58-67 is available at
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