CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

DOI

10.2991/ijcis.11.1.3How to use a DOI?

Keywords

Extreme learning machine; Privacy-preserving machine learning; Homomorphic encryption

Abstract

The training techniques of the distributed machine learning approach replace the traditional methods with a cloud computing infrastructure and provide flexible computing services to clients. Moreover, machine learning-based classification methods are used in many diverse applications such as medical predictions, speech/face recognition, and financial applications. Most of the application areas require security and confidentiality for both the data and the classifier model. In order to prevent the risk of confidential data disclosure while outsourcing the data analysis, we propose a privacy-preserving protocol approach for the extreme learning machine algorithm and give private classification protocols. The proposed protocols compute the hidden layer output matrix H in an encrypted form by using a distributed multi-party computation (or cloud computing model) approach. This paper shows how to build a privacy-preserving classification model from encrypted data.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Ferhat Özgür Çatak
AU  - Ahmet Fatih Mustacoglu
PY  - 2018
DA  - 2018/01/01
TI  - CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems
JO  - International Journal of Computational Intelligence Systems
SP  - 33
EP  - 44
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.3
DO  - 10.2991/ijcis.11.1.3
ID  - Çatak2018
ER  -