Privacy Preserving Aggregation of Secret Classifiers

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.