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Quadratic Error Minimization in a Distributed Environment with Privacy Preserving

  • Gérald Gavin
  • Julien Velcin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6549)

Abstract

In this paper, we address the issue of privacy preserving data-mining. Specifically, we consider a scenario where each member j of T parties has its own private database. The party j builds a private classifier h j for predicting a binary class variable y. The aim of this paper consists in aggregating these classifiers h j in order to improve the individual predictions. Precisely, the parties wish to compute an efficient linear combinations over their classifier in a secure manner.

Keywords

Encryption Scheme Generalization Error Privacy Preserve Quadratic Error Gradient Descent Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gérald Gavin
    • 1
  • Julien Velcin
    • 1
  1. 1.Laboratory ERICUniversity of LyonFrance

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