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Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing

Authors:

Naixue XiongAuthors Info & Claims

ACM Transactions on Embedded Computing Systems, Volume 23, Issue 1

Article No.: 7, Pages 1 - 31

https://doi.org/10.1145/3582008

Published: 10 January 2024 Publication History

Abstract

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.

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Cited By

Sun ZLiu AXiong NZhang SWang T(2024)DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal CrowdsourcingIEEE Transactions on Services Computing10.1109/TSC.2024.3451236(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3451236
Zhang SWang RWang HDeng ZYang ZWu D(2024)Bilateral Task-Driven Privacy-Preserving Data Acquisition for Crowdsensed Data TradingIEEE Internet of Things Journal10.1109/JIOT.2023.332438411:6(9766-9780)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3324384
Xu HHe ZLan D(2024)Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supplyFuture Generation Computer Systems10.1016/j.future.2024.06.061161(106-118)Online publication date: Dec-2024
https://doi.org/10.1016/j.future.2024.06.061
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Index Terms

Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing
1. Security and privacy
  1. Cryptography
    1. Symmetric cryptography and hash functions
      1. Hash functions and message authentication codes

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cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 23, Issue 1

January 2024

406 pages

EISSN:1558-3465

DOI:10.1145/3613501

Editor:
Tulika Mitra
National University of Singapore, Singapore

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 January 2024

Online AM: 25 January 2023

Accepted: 17 January 2023

Revised: 17 January 2023

Received: 22 September 2022

Published in TECS Volume 23, Issue 1

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Qinchuangyuan Platform “Scientists+engineers” group building project

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Cited By

Sun ZLiu AXiong NZhang SWang T(2024)DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal CrowdsourcingIEEE Transactions on Services Computing10.1109/TSC.2024.3451236(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3451236
Zhang SWang RWang HDeng ZYang ZWu D(2024)Bilateral Task-Driven Privacy-Preserving Data Acquisition for Crowdsensed Data TradingIEEE Internet of Things Journal10.1109/JIOT.2023.332438411:6(9766-9780)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3324384
Xu HHe ZLan D(2024)Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supplyFuture Generation Computer Systems10.1016/j.future.2024.06.061161(106-118)Online publication date: Dec-2024
https://doi.org/10.1016/j.future.2024.06.061
Agrawal ABhatia ATiwari K(2024)Enhancing Mobile Crowdsensing Security: A Proof of Stake-Based Publisher Selection Algorithm to Combat Sybil Attacks in Blockchain-Assisted MCS SystemsAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_16(174-186)Online publication date: 9-Apr-2024
https://doi.org/10.1007/978-3-031-57916-5_16
Wu HWang SDong GZhou MLiu YZhang L(2023)Data Sharing System Based on Blockchain to Enhance The Visible of Meta Data Information in Privacy Computing2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00048(296-302)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00048

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