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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 hj for predicting a binary class variable y. The aim of this paper consists of aggregating these classifiers hj 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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ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; U. of Skövde; PO Box 408; 54128 Skövde; (Sweden); e-mail:tdp@tdp.cat

 


Vicenç Torra, Last modified: 10 : 44 June 27 2015.