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IEEE Final Year Project Topic for CSE

Base Paper Title

Preserving Model Privacy for Machine Learning in Distributed Systems

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IEEE Project Abstract

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.

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