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

Shao，Yan1; Li，Zhanjun1; Hong，Wenjing2

10.1117/12.2540453

2019

0277-786X

1996-756X

Proceedings of SPIE - The International Society for Optical Engineering

11198

Nanjing, China

1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA

SPIE

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.

Data mining Distributed Polynomial regression Privacy preserving Secure

其他

英语

CPCI-S ; EI

Computer Science ; Optics

Computer Science, Artificial Intelligence ; Optics

WOS:000502121300016

20193907478821

Data mining ; Pattern recognition ; Polynomials ; Regression analysis

Data Processing and Image Processing:723.2 ; Algebra:921.1 ; Mathematical Statistics:922.2

2-s2.0-85072637377

Scopus

1.School of Computer Science and TechnologyUniversity of Science and Technology of China,Hefei,China
2.Department of Computer Science and EngineeringSouthern University of Science and Technology,Shenzhen,China

Shao，Yan,Li，Zhanjun,Hong，Wenjing. A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment[C]. 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA:SPIE,2019.