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IEEE Final Year Project Topics for ECE

Base Paper Title

Preserving Model Privacy for Machine Learning in Distributed Systems

Our Title

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.

Existing System

Drawback of Existing System

Proposed System

Advantage of Proposed System

Enhancement from Base Paper

Architecture

Technology Used : Hardware & Software

Existing Algorithm

Proposed Algorithm

Advantages of Proposed Algorithm

Project Modules

Literature Survey

Conclusion

Future Work

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