International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Jiao Bo 1,2*, Manual Selvaraj Bexci 1

1 Faculty of Social Science, Art & Humanities, Lincoln University College, Malaysia

2 Faculty of Art, Zhengzhou Business University, China


 

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ABSTRACT


The virtual simulation laboratory saves the material resources and equipment investment required for simulating a real experiment environment. This paves the learners to experiment and explore in a virtual environment, reducing resource waste and cost. In addition, the virtual simulation laboratory can also realize the sharing of resources, and academic institutions can share the platform and content of the virtual laboratory to improve the efficiency of resource utilization. But the virtual simulation experiment data can be is easily hacked from the network, hence making it challenging task to study virtual simulation data security. In this paper, we research the virtual simulation data security based on deep learning through applied innovation design and proposed a new algorithm. The minimum violation sequence set in the virtual simulation data set is identified and the suppression mode of the minimum violation sequence is judged. The score table is constructed for the instances in the sequence, and the corresponding instances are selected and suppressed according to the score value. The cross-attention module of Transformer Structure is proposed to aggregate the global and local feature information between left and right graphs and obtain the long-distance dependence relationship between left and right graphs along the polar direction, which can more effectively fuse the global feature information of left and right graphs. The results show that the proposed algorithm can not only ensure the safety of trajectory data but also improve the availability of data.


Keywords: Applied innovation design, Deep learning, Virtual simulation, Data security.


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REFERENCES


  1. Adnan, M., Kalra, S., Cresswell, J.C., Taylor, G.W., Tizhoosh, H.R. 2022. Federated learning and differential privacy for medical image analysis. Scientific Reports, 12, 1953.

  2. Bag, S., Gupta, S., Kumar, A., Sivarajah, U. 2021. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, 178–189.

  3. Birjali, M., Kasri, M., Beni-Hssane, A. 2021. A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.

  4. Brauwers, G., Frasincar, F. 2023. A general survey on attention mechanisms in deep learning. IEEE Transactions on Knowledge and Data Engineering, 35, 3279–3298.

  5. Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., Yiu, T. 2005. MAFIA: A maximal frequent itemset algorithm. IEEE transactions on knowledge and data engineering, 17, 1490–1504.

  6. Chen, R., Fung, M., Mohammed, N., Bipin, C., Wang, K. 2013. Privacy-preserving trajectory data publishing by local suppression. Information Sciences, 231, 83–97.

  7. Cretu, A., Houssiau, F., Cully, A., Montjoye, Y. 2022. QuerySnout: Automating the discovery of attribute inference attacks against query-based systems. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 623–637.

  8. Dhinakaran, D., Joe Prathap, P.M. 2022. Ensuring privacy of data and mined results of data possessor in collaborative ARM. In Pervasive Computing and Social Networking: Proceedings of ICPCSN, 431–444.

  9. Gopi, S., Lee, T., Liu, D. 2022. Private convex optimization via exponential mechanism. In Conference on Learning Theory. PMLR, 1948–1989.

  10. Guo, J., Cao, W., Nie, B., Qin, Q. 2023. Unsupervised learning composite network to reduce training cost of deep learning model for colorectal cancer diagnosis. IEEE Journal of Translational Engineering in Health and Medicine, 11, 54–59.

  11. Hamid, R.A., Albahri, A.S., Alwan, J.K., Al-Qaysi, Z.T., Albahri, O.S., Zaidan, A.A., Alnoor, A., Alamoodi, A.H., Zaidan, B.B. 2021. How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. Computer Science Review, 39, 100337.

  12. Hu, S., Gao, S., Wu, L., Xu, Y., Zhang, Z., Cui, H., Gong, X. 2021. Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach. Computers, Environment and Urban Systems, 87, 101619.

  13. Irazoqui, G., Inci, S., Eisenbarth, T., Sunar, B. 2014. Wait a minute! A fast, cross-VM attack on AES. In Research in Attacks, Intrusions and Defenses: 17th International Symposium, RAID 2014, Springer International Publishing, 299–319.

  14. Jacobs, G., Konrad, C., Berroth, J., Huang, M. 2022. Function-oriented model-based product development. Design Methodology for Future Products: Data Driven, Agile and Flexible, 243–263.

  15. Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., Li, B. 2018. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In 2018 IEEE Symposium on Security and Privacy (SP), 19–35.

  16. Jia, D., Yin, B., Huang, X. 2021. Association analysis of private information in distributed social networks based on big data. Wireless Communications and Mobile Computing, 2021, 1–12.

  17. Kerestes, C., Delafield, R., Elia, J., Chong, E., Kaneshiro, B., Soon, R. 2021. It was close enough, but it wasn't close enough: A qualitative exploration of the impact of direct-to-patient telemedicine abortion on access to abortion care. Contraception, 104, 67–72.

  18. Kesu, S., Ramasangu, H. 2023. Pressure flow dynamics in cellular automata based nephron network model. International Journal of Applied Science and Engineering, 20, 1–12.

  19. Lee, H., Choi, H., Byun, M., Chang, J. 2022. Multi-scale architecture and device-aware data-random-drop based fine-tuning method for acoustic scene classification. In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022).

  20. Li, H., Li, Z., Li, K., Rellermeyer, J., Chen, L., Li, K. 2021. SGD_Tucker: A novel stochastic optimization strategy for parallel sparse tucker decomposition. IEEE Transactions on Parallel and Distributed Systems, 32, 1828–1841.

  21. Li, Q., Fu, Q., Zhu, J., Sun, Y., He, H., Hu, H. 2023. Endophytic bacteria in Ricinus communis L.: Diversity of bacterial community, plant− Growth promoting traits of the isolates and its effect on Cu and Cd speciation in soil. Agronomy, 13, 333.

  22. Li, X., Li, H., Zhu, H., Huang, M. 2019. The optimal upper bound of the number of queries for Laplace mechanism under differential privacy. Information Sciences, 503, 219–237.

  23. Liu, C., Zhang, Y. 2023. Advances and hotspots analysis of value stream mapping using bibliometrics. International Journal of Lean Six Sigma, 14, 190–208.

  24. Liu, Z., Jiang, D., Zhang, C., Zhao, H., Zhao, Q., Zhang, B. 2021. A novel fireworks algorithm for the protein-ligand docking on the autodock. Mobile Networks and Applications, 26, 657–668.

  25. Mahawaga, P., Bertok, P., Khalil, I., Liu, D., Camtepe, S., Atiquzzaman, M. 2022. Local differential privacy for deep learning. IEEE Internet of Things Journal, 7, 5827–5842.

  26. Meng, X., Wang, X., Yin, S., Li, H. 2023. Few-shot image classification algorithm based on attention mechanism and weight fusion. Journal of Engineering and Applied Science, 70.

  27. Nam, H., Lee, C. 2023. Random image frequency aggregation dropout in image classification for deep convolutional neural networks. Computer Vision and Image Understanding, 232, 103684.

  28. Ren, W., Ghazinour, K., Lian, X. 2023. kt-safety: Graph release via k-anonymity and t-closeness. IEEE Transactions on Knowledge and Data Engineering, 35, 9102–9113.

  29. Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., Martínez, S. 2014. Enhancing data utility in differential privacy via microaggregation-based k-anonymity. The VLDB Journal, 23, 771–794.

  30. Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., Martínez, S. 2015. t-closeness through microaggregation: Strict privacy with enhanced utility preservation. IEEE Transactions on Knowledge and Data Engineering, 27, 3098–3110.

  31. Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., Megías, D. 2017. Individual differential privacy: A utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security, 12, 1418–1429.

  32. Tseng, C., Zhang, S. 2023. Heuristics for parallel machine scheduling with GoS eligibility constraints. International Journal of Applied Science and Engineering, 20, 1–12.

  33. Xiao, X., Wang, G., Gehrke, J. 2011. Differential privacy via wavelet transforms. IEEE Transactions on Knowledge and Data Engineering, 23, 1200–1214.

  34. Yin, C., Xi, J., Sun, R., Wang, J. 2018. Location privacy protection based on differential privacy strategy for big data in industrial internet of things. IEEE Transactions on Industrial Informatics, 14, 3628–3636.

  35. Yin, S., Li, H., Liu, D., Karim, S. 2020. Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimedia Tools and Applications, 79, 31049–31068.

  36. Yin, S., Li, H., Laghari, A., Karim, S., Jumani, K. 2021. A bagging strategy-based kernel extreme learning machine for complex network intrusion detection. EAI Endorsed Transactions on Scalable Information Systems. 21, e8.

  37. Yu, Q., Yang, F., Xiao, Z., Gong, S., Sun, L., Chen, C. 2023. Trajectory personalization privacy preservation method based on multi-sensitivity attribute generalization and local suppression. Intelligent Data Analysis, 27, 935–957.

  38. Zhang, D., Shafiq, M., Wang, L., Srivastava, G., Yin, S. 2023. Privacy‐preserving remote sensing images recognition based on limited visual cryptography. CAAI Transactions on Intelligence Technology, 2023, 1–12.

  39. Zhang, K., Tian, J., Xiao, H., Zhao, Y., Zhao, W., Chen, J. 2022. A numerical splitting and adaptive privacy budget-allocation-based LDP mechanism for privacy preservation in blockchain-powered IoT. IEEE Internet of Things Journal, 10, 6733–6741.

  40. Zhao, Y., Chen, J. 2022. A survey on differential privacy for unstructured data content. ACM Computing Surveys (CSUR), 54, 1–28.

  41. Zheng, S., Ren, S., Wang, J., Wang, C., Wang, Y. 2022. Design of network big data anti attack system for carbon emission measurement based on deep learning. International Conference on Machine Learning for Cyber Security, 279–293.


ARTICLE INFORMATION


Received: 2023-09-02
Revised: 2023-09-13
Accepted: 2023-10-04
Available Online: 2023-12-26


Cite this article:

Bo, J., Bexci, M.S. 2024. New algorithm to ensure virtual simulation data security based on deep learning using applied innovation design. International Journal of Applied Science and Engineering, 21, 2023323. https://doi.org/10.6703/IJASE.202403_21(1).002

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