A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment

Yan Shao; Zhanjun Li; Wenjing Hong

doi:10.1117/12.2540453

31 July 2019 A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment

Yan Shao, Zhanjun Li, Wenjing Hong

Author Affiliations +

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980H (2019) https://doi.org/10.1117/12.2540453
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

Abstract

The idea of ensemble learning can be used to solve problems about privacy preserving distributed data mining conveniently. Owners of distributed datasets can get an integrated model securely just by sharing and combining their sub models which are built on their respective sample sets, and generally the integrated model is more powerful than any sub model. However, sharing the sub models may cause serious privacy problems in some cases. So in this paper, we present a new method, based on which the data holders can integrate their sub polynomial regression models securely and efficiently without sharing them, and get the optimal combination regression model. In addition to theoretical analysis, we also verify the availability of the new method through experiments.

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Yan Shao, Zhanjun Li, and Wenjing Hong "A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980H (31 July 2019); https://doi.org/10.1117/12.2540453

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