Collaborative classification mechanism for privacy-Preserving on horizontally partitioned data
Zhancheng Zhang, Fu-Lai Chung, Shitong Wang · Automatika · 2019
We propose a novel two-party privacy-preserving classification solution called Collaborative Classification Mechanism for Privacy-preserving (C2MP2) 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. C2MP2 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 C2MP2 can be transformed into existing Minimax Probability Machine (MPM), Support Vector Machine (SVM) and Maxi–Min Margin Machine (M4) model when privacy data satisfy certain conditions. We also extend C2MP2 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.