Published in

World Scientific Publishing, International Journal on Artificial Intelligence Tools, 05(23), p. 1450007

DOI: 10.1142/s0218213014500079

Links

Tools

Export citation

Search in Google Scholar

Privacy Preserving Data Mining Using Radial Basis Functions on Horizontally Partitioned Databases in the Malicious Model

Journal article published in 2014 by Alexandros Panteli, Manolis Maragoudakis, Stefanos Gritzalis
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

This paper presents a privacy preserving protocol for the computation of a Radial Basis Function (RBF) neural network model between N participants which share horizontally partitioned datasets. The RBF model is used for regression analysis tasks. The novel aspect of the proposed protocol lies to the fact that it assumes a malicious user model and does not use homomorphic cryptographic methods, which are inherently only suited for a semi-trusted user environment. The performance analysis shows that the communication overhead is low enough to warranty its use while the computational complexity is identical in most cases with the centralized computation scenario (e.g. a trusted third party). The accuracy of the output model is only marginally subpar to a centralized computation on the union of all datasets.