Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data

TL;DR

This paper introduces a secure, privacy-preserving extreme learning machine (ELM) algorithm designed for vertically partitioned data, enabling multiple parties to collaboratively build classification models without exposing private data.

Contribution

It proposes a novel secure multi-party computation method for ELM that preserves data privacy in vertically distributed datasets, addressing security concerns in collaborative learning.

Findings

Achieves privacy preservation without data sharing

Maintains high classification accuracy

Ensures secure computation over distributed data

Abstract

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.