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The data transaction reputation evaluation model based on evaluation entity

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Abstract

Aiming at the problem of imprecise reputation evaluation caused by too much subjective and qualitative evaluation of the traditional reputation evaluation model in data transactions, An Evaluation Entity-Based Reputation Evaluation model (EEBRE) is proposed. In order to realize multi-dimensional reputation evaluation, the structure of the evaluation entity is proposed to dynamically obtain the reputation data of the trading parties; a reputation evaluation assembly method combining objective indicators and feedback rating indicators is proposed, which replaces the qualitative scoring of data set quality indicators with quantitative scoring, introduces the static individual information indicators as well as transaction context attribute indicators, such as transaction amount, time, etc., and retains the qualitative evaluation of the feedback ratings to provide an interactive experience feedback pathway for the data requesting party. The experimental results show that the EEBRE model proposed in this paper can safely and effectively evaluate the seller’s reputation.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the figshare repository, https://doi.org/10.6084/m9.figshare.23819655[37]

References

  1. Jones CI, Tonetti C (2020) Nonrivalry and the economics of data. Am Econ Rev 110:2819–2858. https://doi.org/10.1257/aer.20191330

    Article  Google Scholar 

  2. Dai W et al (2020) Sdte: a secure blockchain-based data trading ecosystem. IEEE Trans Inf Forensics Secur 15:725–737. https://doi.org/10.1109/TIFS.2019.2928256

    Article  Google Scholar 

  3. Zheng L et al (2017) Customized data plans for mobile users: feasibility and benefits of data trading. IEEE J Sel Areas Commun 35:949–963. https://doi.org/10.1109/jsac.2017.2679661

    Article  Google Scholar 

  4. Nguyen L et al (2021) Modeling and analysis of data trading on blockchain-based market in iot networks. IEEE Internet Things J 8:6487–6497. https://doi.org/10.1109/jiot.2021.3051923

    Article  Google Scholar 

  5. An B, Xiao M, Liu A, Xu Y, Zhang X, Li Q (2021) Secure crowdsensed data trading based on blockchain. IEEE Trans Mob Comput 22:1763–1778. https://doi.org/10.1109/tmc.2021.3107187

    Article  Google Scholar 

  6. Cheng HK et al (2020) Can gold medal online sellers earn gold? the impact of reputation badges on sales. J Manag Inf Syst 37:1099–1127. https://doi.org/10.1080/07421222.2020.1831776

    Article  Google Scholar 

  7. Song S et al (2005) Trusted p2p transactions with fuzzy reputation aggregation. IEEE Internet Comput 9:24–34. https://doi.org/10.1109/MIC.2005.136

    Article  Google Scholar 

  8. Nwebonyi FN, Martins R, Correia ME (2018) Reputation based approach for improved fairness and robustness in p2p protocols. Peer-to-Peer Network Appl 12:951–968. https://doi.org/10.1007/s12083-018-0701-x

    Article  Google Scholar 

  9. You W et al (2011) Reputation inflation detection in a chinese c2c market. Electron Commer Res Appl 10:510–519. https://doi.org/10.1016/j.elerap.2011.06.001

    Article  Google Scholar 

  10. Priyanka T et al (2015) Web service recom-mendation systems with reputation evaluation and malicious feed-back estimation prevention. Int J Res Comput Appl Robot 3(3):230–235

    Google Scholar 

  11. Zou B et al (2014) Social recommendations based on user trust and tensor factorization. J Softw 25:2852–2864. https://doi.org/10.13328/j.cnki.jos.004725

    Article  MathSciNet  Google Scholar 

  12. Xiong Y, Fu X (2024) User credibility evaluation for reputation measurement of online service. Int J Web Info Syst 20:176–194. https://doi.org/10.1108/IJWIS-12-2023-0247

    Article  Google Scholar 

  13. Guo HH, Jiang JH, Cai H (2009) Modeling for reputation computing in c2c communities. Chin J Manage 6(8):1056–1060

    Google Scholar 

  14. Wang C et al (2014) Trust evaluation model based on reputation for c2c e-commerce. Comput Eng Appl 50(17):120–124244

    Google Scholar 

  15. Liu Y, Zhou X, Yu H (2021) 3r model: a post-purchase context-aware reputation model to mitigate unfair ratings in e-commerce. Knowl-Based Syst 231:107441. https://doi.org/10.1016/j.knosys.2021.107441

    Article  Google Scholar 

  16. Wang C et al (2021) Factors influencing consumers’ purchase decision-making in o2o business model: evidence from consumers’ overall evaluation. J Retail Consum Serv 61:102565. https://doi.org/10.1016/j.jretconser.2021.102565

    Article  Google Scholar 

  17. GAN Z-B et al (2011) Reputation-based multi-dimensional trust algorithm: Reputation-based multi-dimensional trust algorithm. J Softw 22:2401–2411. https://doi.org/10.3724/sp.j.1001.2011.03909

    Article  Google Scholar 

  18. Hu D et al (2021) A blockchain-based trading system for big data. Comput Netw 191:107994. https://doi.org/10.1016/j.comnet.2021.107994

    Article  Google Scholar 

  19. Ji S, et al. (2016) An anti-attack model for centralized c2c reputation evaluation agent. In 2016 IEEE International Conference on Agents (ICA), 63–69, https://doi.org/10.1109/ICA.2016.023

  20. Zhang Y et al (2015) Evaluation model of buyers-dynamic reputation in e-commerce. Int J Multimed Ubiquitous Eng 10:53–64. https://doi.org/10.14257/ijmue.2015.10.5.07

    Article  Google Scholar 

  21. Tang M et al (2017) Towards a trust evaluation middleware for cloud service selection. Futur Gener Comput Syst 74:302–312. https://doi.org/10.1016/j.future.2016.01.009

    Article  Google Scholar 

  22. Ding S et al (2014) Combining qos prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowl-Based Syst 56:216–225. https://doi.org/10.1016/j.knosys.2013.11.014

    Article  Google Scholar 

  23. Li X et al (2019) Enhancing cloud-based iot security through trustworthy cloud service: An integration of security and reputation approach. IEEE Access 7:9368–9383. https://doi.org/10.1109/access.2018.2890432

    Article  Google Scholar 

  24. Aaltonen A, Alaimo C, Kallinikos J (2021) The making of data commodities: data analytics as an embedded process. J Manag Inf Syst 38:401–429. https://doi.org/10.1080/07421222.2021.1912928

    Article  Google Scholar 

  25. Teh HY, Kempa-Liehr AW, Wang KIK (2020) Sensor data quality: a systematic review. J Big Data 7:11. https://doi.org/10.1186/s40537-020-0285-1

    Article  Google Scholar 

  26. Li Z et al (2022) The analysis of two-way e-commerce credit evaluation model based on the c2c mode. J Glob Inf Manag 30:1–21. https://doi.org/10.4018/JGIM.305238

    Article  Google Scholar 

  27. Li Y, Chen L (2020) Risk evaluation for c2c e-commerce via an improved credit counting method. Int Technol Lett 4:222. https://doi.org/10.1002/itl2.222

    Article  Google Scholar 

  28. Wu F, Li HH, Kuo YH (2011) Reputation evaluation for choosing a trustworthy counterparty in c2c e-commerce. Electron Commer Res Appl 10:428–436. https://doi.org/10.1016/j.elerap.2010.09.004

    Article  Google Scholar 

  29. Li C, Liang C (2012) Multi-dimensionality reputation evaluation model for c2c e-commerce. Chin J Manage 9(2):204–211

    Google Scholar 

  30. Liang X, Xiao J (2023) Dual channel based on blockchain and consumer sensitivity supply chain pricing and channel selection. Chin J Manage Sci 31:29–38. https://doi.org/10.16381/j.cnki.issn1003-207x.2020.1755

    Article  Google Scholar 

  31. Kim J, Lennon SJ (2013) Effects of reputation and website quality on online consumers’ emotion, perceived risk and purchase intention. J Res Interact Mark 7:33–56. https://doi.org/10.1108/17505931311316734

    Article  Google Scholar 

  32. Liu J, Wang M (2019) Practice of data quality evaluating index construction under big data. Comput Technol Develop 29(10):46–50

    Google Scholar 

  33. Tadelis S (2016) Reputation and feedback systems in online platform markets. Annu Rev Econ 8:321–340. https://doi.org/10.1146/annurev-economics-080315-015325

    Article  Google Scholar 

  34. Li L, Tadelis S, Zhou X (2016) Buying reputation as a signal of quality: evidence from an online marketplace. Rand J Econ 51:965–988. https://doi.org/10.1111/1756-2171.12346

    Article  Google Scholar 

  35. Wang J, Shan F, Zhang Y (2021) Research on the effectiveness of reputation mechanism of e-commerce platforms and research on credit supervision-evidence from taobao and jingdong. Collect Essays Financ Econ 269:103–112. https://doi.org/10.3969/j.issn.1004-4892.2021.02.010

    Article  Google Scholar 

  36. Xu Y et al (2018) Optimal control of an online reputation dynamic feedback incentive model. Commun Nonlinear Sci Numer Simul 63:1–11. https://doi.org/10.1016/j.cnsns.2018.03.005

    Article  MathSciNet  Google Scholar 

  37. Cao L (2023) Data used and generated in data transactions. Figshare. https://doi.org/10.6084/m9.figshare.23819655

    Article  Google Scholar 

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Funding

This research was supported by the Natural Science Foundation of Shandong Province, research on Key Technologies of Dynamic Game Access Control based on Blockchain, fund number ZR2020MF029, and the Natural Science Foundation of Shandong Province, fund number ZR2020MF058.

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Author notes

  1. Bin Zhao and Li Cao have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Engineering, Linyi University, Linyi, 276000, China

    Bin Zhao, Li Cao & Yilong Gao

  2. Linyi New Smart City Research Institute, Linyi, China

    Bin Zhao

  3. Shandong Linchuang Shugu Information Technology Co., Ltd, Linyi, China

    Yilong Gao

Authors
  1. Bin Zhao
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  2. Li Cao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Li Cao, Yilong Gao and Bin Zhao. The first draft of the manuscript was written by Li Cao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yilong Gao.

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Zhao, B., Cao, L. & Gao, Y. The data transaction reputation evaluation model based on evaluation entity. J Supercomput 80, 25377–25402 (2024). https://doi.org/10.1007/s11227-024-06415-z

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