Especially in the Big Data era, the usage of different classification methodsis increasing day by day. The success of these classification methods dependson the effectiveness of learning methods. Extreme learning machine (ELM)classification algorithm is a relatively new learning method built onfeed-forward neural-network. ELM classification algorithm is a simple and fastmethod that can create a model from high-dimensional data sets. Traditional ELMlearning algorithm implicitly assumes complete access to whole data set. Thisis 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 forELM classification over data that is vertically partitioned among severalparties. The new learning method preserves the privacy on numerical attributes,builds a classification model without sharing private data without disclosingthe data of each party to others.
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