A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 12
Dec.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
J. Li, J. Li, X. Wang, R. Qin, Y. Yuan, and  F.-Y. Wang,  “Multi-blockchain based data trading markets with novel pricing mechanisms,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2222–2232, Dec. 2023. doi: 10.1109/JAS.2023.123963
Citation: J. Li, J. Li, X. Wang, R. Qin, Y. Yuan, and  F.-Y. Wang,  “Multi-blockchain based data trading markets with novel pricing mechanisms,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2222–2232, Dec. 2023. doi: 10.1109/JAS.2023.123963

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

doi: 10.1109/JAS.2023.123963
Funds:  This work was partially supported by the Science and Technology Development Fund, Macau SAR (0050/2020/A1) and the National Natural Science Foundation of China (62103411, 72171230)
More Information
  • In the era of big data, there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data. Data security and data pricing, however, are still widely regarded as major challenges in this respect, which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms. In this context, data recording and trading are conducted separately within two separate blockchains: the data blockchain (DChain) and the value blockchain (VChain). This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market. Moreover, pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users. Specifically, in regular data trading on VChain-S2D, two auction models are employed according to the demand scale, for dealing with users’ strategic bidding. The incentive-compatible Vickrey-Clarke-Groves (VCG) model is deployed to the low-demand trading scenario, while the nearly incentive-compatible monopolistic price (MP) model is utilized for the high-demand trading scenario. With temporary data trading on VChain-D2S, a reverse auction mechanism namely two-stage obscure selection (TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies. Furthermore, experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.


  • loading
  • [1]
    F.-Y. Wang, J. Yang, X. Wang, J. Li, and Q.-L. Han, “Chat with ChatGPT on Industry 5.0: Learning and decision-making for intelligent industries,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 831–834, Apr. 2023. doi: 10.1109/JAS.2023.123552
    X. X. Wang, J. Yang, Y. T. Wang, Q. H. Miao, F.-Y. Wang, A. J. Zhao, J.-L. Deng, L. X. Li, X. X. Na, and L. Vlacic, “Steps toward Industry 5.0: Building “6S” parallel industries with cyber-physical-social intelligence,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1692–1703, Aug. 2023. doi: 10.1109/JAS.2023.123753
    Y. Zhou, X. Luo, and M. Zhou, “Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1105–1121, 2023. doi: 10.1109/JAS.2023.123450
    P. Y. Zhang, M. C. Zhou, C. X. Li, and A. Abusorrah, “Dynamic evolutionary game-based modeling, analysis and performance enhancement of blockchain channels,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 188–202, 2023. doi: 10.1109/JAS.2022.105911
    J. Li, R. Qin, and F.-Y. Wang, “The future of management: DAO to smart organizations and intelligent operations,” IEEE Trans. Systems,Man,and Cyber.: Syst., vol. 53, no. 6, pp. 3389–3399, 2023. doi: 10.1109/TSMC.2022.3226748
    R. Qin, W. Ding, J. Li, S. Guan, and Z. Qu, “Web3-based decentralized autonomous organizations and operations: Architectures, models and mechanisms,” IEEE Trans. Systems,Man,and Cyber.: Syst., vol. 53, no. 4, pp. 2073–2082, 2023. doi: 10.1109/TSMC.2022.3228530
    J. Frizzo-Barker, P. Chow-White, P. Adams, J. Mentanko, D. Ha, and S. Green, “Blockchain as a disruptive technology for business: A systematic review,” Int. J. Information Management, vol. 51, p. 102029, 2020. doi: 10.1016/j.ijinfomgt.2019.10.014
    M. Mainelli, “Blockchain could help us reclaim control of our personal data,” Harvard Business Review. [Online], Available: https://hbr.org/2017/10/smart-ledgers-can-help-us-reclaim-control-of-our-personal-data, Oct. 5, 2017.
    T. Choi, S. Guo, N. Liu, and X. Shi, “Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era,” European J. Operational Research, vol. 28, no. 3, pp. 1031–1042, 2020.
    S. Delgado-Segura, C. Pérez-Solà, G. Navarro-Arribas, et al., “A fair protocol for data trading based on Bitcoin transactions,” Future Generation Computer Systems, vol. 107, pp. 832–840, 2020. doi: 10.1016/j.future.2017.08.021
    D. Hu, Y. Li, L. Pan, M. Li, and S. Zheng, “A blockchain-based trading system for big data,” Computer Networks, vol. 191, p. 107994, 2021. doi: 10.1016/j.comnet.2021.107994
    M. Qi, Z. Wang, Q.-L. Han, J. Zhang, S. Chen and Y. Xiang, “Privacy protection for blockchain-based healthcare IoT systems: A survey,” IEEE/CAA J. Autom. Sinica, DOI: 10.1109/JAS.2022.106058.
    Y. Zhao, Y. Yu, Y. Li, G. Han, and X. Du, “Machine learning based privacy-preserving fair data trading in big data market,” Information Sciences, vol. 478, pp. 449–460, 2019. doi: 10.1016/j.ins.2018.11.028
    J. Wang, Z. Zheng, F. Wu, et al., “Blockchain based data marketplace,” Big Data Research, vol. 6, no. 3, pp. 21–35, 2020.
    P. Koutris, P. Upadhyaya, M. Balazinska, et al., “Query-based data pricing,” J. the ACM, vol. 62, no. 5, pp. 1–44, 2015.
    J. Liang and C. Yuan, “Data price determinants based on a hedonic pricing model,” Big Data Research, vol. 25, p. 100249, 2021. doi: 10.1016/j.bdr.2021.100249
    J. Pei, “A survey on data pricing: From economics to data science,” IEEE Trans. Knowledge and Data Engineering, vol. 34, no. 10, pp. 4586–4608, 2022. doi: 10.1109/TKDE.2020.3045927
    B. Li, Q. Yang, and I. Kamwa, “A novel stackelberg-game-based energy storage sharing scheme under demand charge,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 462–473, 2023. doi: 10.1109/JAS.2023.123216
    B. Zhang, C. X. Dou, D. Yue, J. H. Park, Y. D. Zhang, and Z. Q. Zhang, “Game and dynamic communication path-based pricing strategies for microgrids under communication interruption,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1032–1047, 2023. doi: 10.1109/JAS.2023.123138
    K Liu, X Qiu, W Chen, X Chen, and Z Zheng, “Optimal pricing mechanism for data market in blockchain-enhanced Internet of Things,” IEEE Internet of Things J., vol. 6, no. 6, pp. 9748–9761, 2019. doi: 10.1109/JIOT.2019.2931370
    Z. Li, Z. Yang, and S. Xie, “Computing resource trading for edge-cloud-assisted Internet of things,” IEEE Trans. Industrial Informatics, vol. 15, no. 6, pp. 3661–3669, 2019. doi: 10.1109/TII.2019.2897364
    L. Duan, Y. Y. Sun, W. Ni, W. P. Ding, J. Q. Liu, and W. Wang, “Attacks against cross-chain systems and defense approaches: A contemporary survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1643–1663, 2023. doi: 10.1109/JAS.2023.123768
    S. Dustdar, P. Fernández, J. M. García, and A. Ruiz-Cortés, “Elastic smart contracts in blockchains,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1901–1912, 2021. doi: 10.1109/JAS.2021.1004222
    L. Ausubel and P. Milgrom, “The lovely but lonely Vickery auction,” in Combinatorial Auctions, Cambridge, USA: The MIT Press, pp. 17–40, 2006.
    V. Krishna, Auction Theory. Cambridge, UK: Academic Press, 2009.
    R. Lavi, O. Sattath, and A. Zohar, “Redesigning Bitcoin’s fee market,” The World Wide Web Conf.,ACM, pp. 2950–2956, 2950.
    J. Li, Y. Yuan, and F.-Y. Wang, “A novel GSP auction mechanism for ranking Bitcoin transactions in blockchain mining,” Decision Support Systems, vol. 124, p. 113094, 2019. doi: 10.1016/j.dss.2019.113094
    J. Li, X. Ni, Y. Yuan, and F.-Y. Wang, “A novel GSP auction mechanism for dynamic confirmation games on Bitcoin transactions,” IEEE Trans. Services Computing, vol. 15, no. 3, pp. 1436–1447, 2022. doi: 10.1109/TSC.2020.2994582
    F.-Y. Wang, R. Qin, X. Wang, and B. Hu, “MetaSocieties in Metaverse: MetaEconomics and MetaManagement for MetaEnterprises and MetaCities,” IEEE Trans. Computational Social Systems, vol. 9, no. 1, pp. 2–7, 2022. doi: 10.1109/TCSS.2022.3145165
    F.-Y. Wang, “The DAO to MetaControl for MetaSystems in Metaverses: The system of parallel control systems for knowledge automation and control intelligence in CPSS,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1899–1908, 2022. doi: 10.1109/JAS.2022.106022
    J. Li, R. Qin, W. Ding, G. Wang, T. Wang, and F.-Y. Wang, “A new framework for Web3-powered decentralized autonomous organizations and operations,” Acta Autom. Sinica, vol. 49, no. 5, pp. 985–998, 2023.
    F.-Y. Wang, Q. Miao, X. Li, X. Wang, and Y. Lin, “What does ChatGPT say: The DAO from algorithmic intelligence to linguistic intelligence,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 575–579, 2023. doi: 10.1109/JAS.2023.123486
    F.-Y. Wang, “Parallel intelligence in metaverses: Welcome to HANOI!,” IEEE Intelligent Systems, vol. 37, no. 1, pp. 16–20, 2022. doi: 10.1109/MIS.2022.3154541
    Q. Miao, W. Zheng, Y. Lv, M. Huang, W. Ding, and F.-Y. Wang, “DAO to HANOI via DeSci: AI paradigm shifts from AlphaGo to ChatGPT,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 877–897, 2023. doi: 10.1109/JAS.2023.123561


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (162) PDF downloads(77) Cited by()


    • Establish multi-blockchain based data trading markets to enhance security and efficiency
    • Use the VCG and MP auction mechanism to induce truthful bidding in regular data trading
    • Design the TSOS mechanism to regulate strategic trading behaviors in temporary data trading


    DownLoad:  Full-Size Img  PowerPoint