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Efficient and secure data sharing for 5G flying drones: A blockchain-enabled approach

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journal contribution
posted on 2025-05-13, 14:06 authored by Chaosheng Feng, Keping Yu, Ali Kashif Bashir, Yasser D Al-Otaibi, Yang LuYang Lu, Shengbo Chen, Di Zhang

The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public key cryptography for providing accounts and ensuring accounts' security, and a distributed ledger for security audit. In addition, to speed up out-sourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared to serial computation

Funding

National Natural Science Foundation of China (Grant Number: 61373163)

Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044

History

School

  • Science

Published in

IEEE Network

Volume

35

Issue

1

Pages

130 - 137

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2021-02-16

Copyright date

2021

ISSN

0890-8044

eISSN

1558-156X

Language

  • en

Depositor

Dr Yang Lu. Deposit date: 19 April 2025