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A composite blockchain associated event traceability method for financial activities

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An Author Correction to this article was published on 27 December 2023

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Abstract

The existing blockchain system mostly adopts the equal mining mode. All bookkeepers (entities) record the ledgers on a single main chain, and the data storage is random. Moreover, in complex or classified financial scenarios, the data of the main chain is difficult to realize association or regular storage, resulting in low efficiency of storage and query. At the same time, in the existing blockchain system, event traceability is mostly only found in the source block, and the implicit association between entities cannot be identified, so the query has limitations. To solve these problems, this paper proposes a composite blockchain associated event tracing method. This method firstly constructs the blockchain composite chain storage structure model, proposes the concept of private chain and alliance chain, and realizes the adaptive data association storage in complex or classified scenarios. Secondly, on the basis of obtaining the event source entity block, the auxiliary storage space is established to transfer storage relevant data. A tracing method of associated entity block based on the Apriori algorithm is proposed, and then the obtained traceability entity block is constructed as the source event correlation graph, so as to describe the association relationship between the event entities. Finally, a risk assessment system based on reinforcement learning is proposed to realize the risk assessment of traceability entity. Experiments show that the composite blockchain associated event tracing method proposed in this paper can reduce 60% of the storage overhead, improve 90% of the query accuracy and 50% of the security. can reduce the storage overhead by 60%, improve the query accuracy by 90% and improve security by 50%.

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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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References

  1. Oscar G, Andrés V (2022) The geometry of suspicious money laundering activities in financial networks. European Physical Journal Data Science 11(1):6–15

    Google Scholar 

  2. Zeng H (2022) Influences of mobile edge computing-based service preloading on the early-warning of financial risks. J Supercomput 78(9):11621–11639

    Article  Google Scholar 

  3. Wan PK, Huang L, Holtskog H (2020) Blockchain-enabled information sharing within a supply chain: A systematic literature review. IEEE Access 8:49645–49656

    Article  Google Scholar 

  4. Cai T, Lin H, Chen WH et al (2021) Blockchain-enabled efficient data sharing scheme for internet of things. Journal of Software 32(4):11–23

    Google Scholar 

  5. Ge Y, Huang CL, Chen M et al (2021) HACCP quality traceability model and system implementation based on blockchain. Transactions of the Chinese Society for Agricultural Machinery 52(06):369–375

    Google Scholar 

  6. Cao S, Dang S, Zhang Y et al (2021) A blockchain-based access control and intrusion detection framework for satellite communication systems. Comput Commun 172:216–225

    Article  Google Scholar 

  7. Lim M, Li Y, Wang C et al (2021) A literature review of blockchain technology applications in supply chains: A comprehensive analysis of themes, methodologies and industries. Comput Ind Eng 154:107133–107147

    Article  Google Scholar 

  8. Shae Z, Tsai J (2019) AI Blockchain platform for trusting news. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) 1610–1619

  9. Wang S, Dinh TA, Lin Q et al (2018) ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications. Proceedings of the VLDB Endowment 11(10):1137–1150

    Article  Google Scholar 

  10. Halpin H, Piekarska M (2017) Introduction to security and privacy on the blockchain. IEEE European Symposium on Security & Privacy Workshops. IEEE 1–3

  11. Kraft D (2016) Difficulty control for blockchain-based consensus systems. Peer-to-Peer Networking and Applications 9(2):397–413

    Article  Google Scholar 

  12. Hara K, Takahashi T, Ishimaki M et al (2021) Machine-learning approach using solidity bytecode for smart-contract honeypot detection in the ethereum. International Conference on Software Quality, Reliability and Security (QRS) 652–659

  13. Dinh T T A, Wang J, Chen G et al (2017) BLOCKBENCH: a framework for analyzing private blockchains. Int Conf Manage Data 1085–1100

  14. Cao Y, Miao ZG (2016) An improved apriori algorithm for frequent terms optimization based on vector matrix. Journal of Jilin University (Natural Science Edition) 54(02):349–353

    Google Scholar 

  15. Bian GQ, Wang Y (2017) An improved apriori algorithm based on matrix and weight. Microelectronics & Computer 34(1):137–140

    Google Scholar 

  16. Shao QF, Jin CQ, Zhang Z et al (2018) Blockchain Technology: Architecture and Progress. Chinese J Comp 041(005):969–988

    Google Scholar 

  17. Wang QG, He P, Nie TZ et al (2018) Overview of blockchain system data storage and query technology. Computer Science 45(012):12–18

    Google Scholar 

  18. Wang ZH, Liu PZ, Song CB et al (2020) Research on Flexible trustable traceability system of agricultural products based on blockchain. Comput Eng 46(12):319–326

    Google Scholar 

  19. Belchior R, Vasconcelos A, Guerreiro S et al (2022) A Survey on blockchain interoperability: Past, Present, and Future Trends. ACM Computing Surveys 54(8):168:1–168:41

  20. Li Y, Zheng K, Yan Y et al (2017) Ether QL: a query layer for blockchain system. International Conference on Database Systems for Advanced Applications 556–567

  21. Qi L, Xiao Z (2019) Research on trust mechanism of cooperation innovation with big data processing based on blockchain. EURASIP J Wirel Commun Netw 26(1):17535–17542

    Google Scholar 

  22. Asheralieva A, Niyato D (2020) Reputation-based coalition formation for secure self-organized and scalable sharding in IoT blockchains with mobile-edge computing. IEEE Internet Things J 7(12):11830–11850

    Article  Google Scholar 

  23. Liu LJ (2017) Research and application of improved Apriori algorithm. Computer Engineering and Design 12:142–146

    Google Scholar 

  24. Fan Y, Lin X, Liang W et al (2022) TraceChain: A blockchain-based scheme to protect data confidentiality and traceability. Software - Practice and Experience 52(1):115–129

    Article  Google Scholar 

  25. Pang Y, Wang DS, Wang XY et al (2022) Blockchain-based reliable traceability system for telecom big data transactions. IEEE Internet Things J 9(14):12799–12812

    Article  Google Scholar 

  26. Cao Y, Jia F, Manogaran G (2020) Efficient traceability systems of steel products using blockchain-based industrial internet of things. IEEE Trans Industr Inf 16(9):6004–6012

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Applied Basic Research Program of Liaoning Province(No.2022JH2/101300250); the Digital Liaoning Smart Building Strong Province (Direction of Digital Economy)(No.13031307053000568); the National Key R&D Program of China(No.2021YFF0901004); the Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province (No.2022JH6/100100032); the Natural Science Foundation of Liaoning Province (No.2022-KF-13-06).

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Authors and Affiliations

  1. School of Information, Liaoning University, Shenyang, 110036, China

    Junlu Wang, Su Li, Ji Wanting, Dong Li & Baoyan Song

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  1. Junlu Wang
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  3. Ji Wanting
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Contributions

Author Junlu Wang and Haolin Zhang are responsible for literature collection and writing. Author Su Li and Wanting Ji are responsible for writing ideas and revising the paper. Author Junlu Wang, Su Li, Wanting Ji, Haolin Zhang and Baoyan Song are responsible for giving guidance and revising the paper. All authors reviewed the manuscript and agreed with the content.

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Correspondence to Baoyan Song.

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Wang, J., Li, S., Wanting, J. et al. A composite blockchain associated event traceability method for financial activities. Peer-to-Peer Netw. Appl. 16, 1696–1715 (2023). https://doi.org/10.1007/s12083-023-01469-x

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