  Especially in the Big Data era, the usage of different classification methods
is increasing day by day. The success of these classification methods depends
on the effectiveness of learning methods. Extreme learning machine (ELM)
classification algorithm is a relatively new learning method built on
feed-forward neural-network. ELM classification algorithm is a simple and fast
method that can create a model from high-dimensional data sets. Traditional ELM
learning algorithm implicitly assumes complete access to whole data set. This
is a major privacy concern in most of cases. Sharing of private data (i.e.
medical records) is prevented because of security concerns. In this research,
we propose an efficient and secure privacy-preserving learning algorithm for
ELM classification over data that is vertically partitioned among several
parties. The new learning method preserves the privacy on numerical attributes,
builds a classification model without sharing private data without disclosing
the data of each party to others.
