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Volume 4 Issue 3

Privacy Preserving Aggregation of Secret Classifiers

Gérald Gavin^(a),(*), Julien Velcin^(a), Philippe Aubertin^(b)

Transactions on Data Privacy 4:3 (2011) 167 - 187

Abstract, PDF

(a) ERIC Lab - University of Lyon - 5; avenue Pierre Mendès France.

(b) Axopen, 17; lot colline du Châtel; 01120 Dagneux; France.

e-mail:gavin @univ-lyon1.fr; julien.velcin @univ-lyon2.fr; aubertinp @gmail.com

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 of aggregating these classifiers h_j in order to improve individual predictions. More precisely, the parties wish to compute an efficient linear combination over their classifier in a secure manner.

^* Corresponding author.

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