Share Email Print

Proceedings Paper

A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment
Author(s): Yan Shao; Zhanjun Li; Wenjing Hong
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980H (31 July 2019); doi: 10.1117/12.2540453
Show Author Affiliations
Yan Shao, Univ. of Science and Technology of China (China)
Zhanjun Li, Univ. of Science and Technology of China (China)
Wenjing Hong, Southern Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?