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Confidential information protection method of commercial information physical system based on edge computing

  • S.I. : ATCI 2020
  • Published:
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Abstract

With the rapid integration and wide application of the enterprise Internet of Things, big data and 5G-class large networks, traditional enterprise cloud computing systems cannot timely process various mass information data generated by connecting with network edge electronic devices. There are obvious technical disadvantages. In order to effectively solve this complex problem, edge mobile computing came into being. The purpose of this article is to study the protection methods of commercial confidential information, using the security relationship between data information in edge technology computing and the technical characteristics of data privacy information protection. This paper proposes a theoretical system and technical architecture centered on the use of data security technology. The three key technologies of access control, identity authentication and information privacy security protection are researched on the privacy protection processing methods of commercial information security physical systems. The experimental data show that the recognition errors mainly occur in identifying non-anomalous data as abnormal data. The analysis and identification of abnormal information data are basically accurate, and it can quickly complete various processing tasks to eliminate abnormal information data to meet the requirements. The experimental data show that the abnormal data can be used to monitor the security of the commercial information physical system, and it is also a good security protection method for commercial confidential information. It has guiding significance for the confidential information protection of commercial information physical systems. In the next few years, more than 50% of major data applications will need to be analyzed, processed and data stored at the edge of the network. Cloud computing technologies at the edge are widely used.

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References

  1. Newman R, Chang V, Walters RJ (2016) Model and experimental development for business data science. Int J Inf Manag 36(4):607–617

    Article  Google Scholar 

  2. Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

    Article  Google Scholar 

  3. Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81

    Article  Google Scholar 

  4. Rankothge W, Le F, Russo A (2017) Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms. IEEE Trans Netw Serv Manag 14(2):343–356

    Article  Google Scholar 

  5. Huang T (2019) Research on cluster mining algorithms for personal privacy protection in the background of big data. J Phys Conf Ser 1314(1):012153

    Article  Google Scholar 

  6. Xie PS, Fu TX, Fan HJ (2019) An algorithm of the privacy security protection based on location service in the internet of vehicles. Int J Netw Secur 21(4):556–565

    Google Scholar 

  7. O’Brien LL, Guo Q, Bahrami-Samani E (2018) Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies. PLoS Genet 14(1):e1007181

    Article  Google Scholar 

  8. Martin KD, Borah A, Palmatier RW (2017) Data privacy: effects on customer and firm performance. J Mark 81(1):36–58

    Article  Google Scholar 

  9. He Y, Zhang Y, Wang X (2020) A new image encryption algorithm based on two-dimensional spatiotemporal chaotic system. Neural Comput Appl 32:247–260

    Article  Google Scholar 

  10. Cheng J, Xu R, Tang X (2018) An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. Comput Mater Contin 55(1):95–119

    Google Scholar 

  11. Sabella D, Vaillant A, Kuure P (2016) Mobile-edge computing architecture: the role of MEC in the Internet of Things. IEEE Consum Electron Mag 5(4):84–91

    Article  Google Scholar 

  12. Li Y, Yu Y, Min G (2017) Fuzzy identity-based data integrity auditing for reliable cloud storage systems. IEEE Trans Dependable Secure Comput 16(1):72–83

    Article  Google Scholar 

  13. Al-Hazaimeh OM, Al-Jamal MF, Alhindawi N et al (2019) Image encryption algorithm based on Lorenz chaotic map with dynamic secret keys. Neural Comput Appl 31:2395–2405

    Article  Google Scholar 

  14. Kalaiprasath R, Elankavi R, Udayakumar DR (2017) Cloud security and compliance—a semantic approach in end to end security. Int J Mech Eng Technol 8(5):987–994

    Google Scholar 

  15. Cohen J, Lefevre J, Maâmra K (2016) A self-stabilizing algorithm for maximal matching in anonymous networks. Parallel Process Lett 26(04):1650016

    Article  MathSciNet  Google Scholar 

  16. Castiglione A, Palmieri F, Chen CL (2016) A blind signature-based approach for cross-domain authentication in the cloud environment. Int J Data Wareh Min (IJDWM) 12(1):34–48

    Article  Google Scholar 

  17. Mattos DMF, Duarte OCMB (2016) AuthFlow: authentication and access control mechanism for software defined networking. Ann Telecommun 71(11–12):607–615

    Article  Google Scholar 

  18. Goulas KA, Gunbas G, Dietrich PJ (2017) ABE condensation over monometallic catalysts: catalyst characterization and kinetics. ChemCatChem 9(4):677–684

    Article  Google Scholar 

  19. Jantsch P, Webster CG, Zhang G (2019) On the Lebesgue constant of weighted Leja points for Lagrange interpolation on unbounded domains. IMA J Numer Anal 39(2):1039–1057

    Article  MathSciNet  Google Scholar 

  20. Chen L, Chen X, Ni L (2017) Human behavior recognition using Wi-Fi CSI: challenges and opportunities. IEEE Commun Mag 55(10):112–117

    Article  Google Scholar 

  21. Kuo CFJ, Juang Y (2016) A study on the recognition and classification of embroidered textile defects in manufacturing. Text Res J 86(4):393–408

    Article  Google Scholar 

  22. Liu Y, Kuang Y, Xiao Y (2017) SDN-based data transfer security for Internet of Things. IEEE Internet Things J 5(1):257–268

    Article  Google Scholar 

  23. Cheng B, Li TY, Wei PC (2018) Layer-edge device of two-dimensional hybrid perovskites. Nat Commun 9(1):1–7

    Article  Google Scholar 

  24. Wang S, Sun S, Li Z (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol 13(1):e1005324

    Article  Google Scholar 

  25. Liu H, Kou H, Yan C (2019) Link prediction in paper citation network to construct paper correlation graph. EURASIP J Wirel Commun Netw 2019(1):1–12

    Article  Google Scholar 

  26. Wenwen G, Lianyong Q, Yanwei X (2018) Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wirel Commun Mob Comput 2018(4):1–8

    Google Scholar 

  27. Chai X, Fu X, Gan Z et al (2020) An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata. Neural Comput Appl 32:4961–4988

    Article  Google Scholar 

  28. Zhang X, Zhou S, Fang J, Ni Y (2020) Pattern recognition of construction bidding system based on image processing. Comput Syst Sci Eng 35(4):247–256

    Article  Google Scholar 

  29. Song H, Srinivasan R, Sookoor T et al (2017) Smart cities: foundations, principles, and applications. Wiley, New York

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by Social Science Foundation of Shaanxi Province “Sustainable trade promotion of cross border e-commerce in China’s Silk Road Economic Belt” (2019S037) and the Young Academic Innovation Team of Northwest University of Political Science and Law.

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Authors and Affiliations

  1. School of Economics and Management, Chuzhou University, Chuzhou, 239000, Anhui, China

    Xiaheng Zhang

  2. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China

    Jiazhong Lu

  3. Business School, Northwest University of Political Science and Law, Xi’an, 710122, Shaanxi, China

    Doudou Li

Authors
  1. Xiaheng Zhang
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  2. Jiazhong Lu
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  3. Doudou Li
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Correspondence to Jiazhong Lu.

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Zhang, X., Lu, J. & Li, D. Confidential information protection method of commercial information physical system based on edge computing. Neural Comput & Applic 33, 897–907 (2021). https://doi.org/10.1007/s00521-020-05272-0

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  • DOI: https://doi.org/10.1007/s00521-020-05272-0

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