Privacy preserving extreme learning machine classification model for distributed systems

Machine learning based classification methods are widely used to analyze large scale datasets in this age of big data. Extreme learning machine (ELM) classification algorithm is a relatively new method based on generalized single-layer feedforward network structure. 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 proposed 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.