Open Access

ARTICLE

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

Bao Le Nguyen¹, E. Laxmi Lydia², Mohamed Elhoseny³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, Mahmoud Mohamed Selim⁶, Gia Nhu Nguyen^{7, 8}, K. Shankar^{9, *}

1 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam.
2 Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India.
3 Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
4 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 115093, Russia.
5 Department of Logistics, State University of Management, Moscow, 109542, Russia.
6 Department of Mathematics, College of Science & Humanities in Alaflaj, Prince Sattam bin Abdulaziz University, Alaflaj, 16278, Saudi Arabia.
7 Graduate School, Duy Tan University, Da Nang, 550000, Vietnam.
8 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam.
9 Department of Computer Applications, Alagappa University, Karaikudi, 630003, India.

* Corresponding Author: K. Shankar. Email: .

Computers, Materials & Continua 2020, 65(1), 87-107. https://doi.org/10.32604/cmc.2020.011599

Received 18 May 2020; Accepted 15 July 2020; Issue published 23 July 2020

Abstract

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

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Citations