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.