Generative Architecture for Data Imputation in Secure Blockchain-enabled Spatiotemporal Data Management

Authors

  • Song Li School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China
  • WenFen Liu School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China
  • Yan Wu Unit 95795 of PLA, Guilin, China
  • Jie Zhao School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2315

Keywords:

Indirect feedback graph algorithm, linked spatiotemporal data, big data analysis, secure access, data retrieval, generative AI, blockchain

Abstract

In the era of big data, one of the most critical challenges is ensuring secure access, retrieval, and sharing of linked spatiotemporal data. To address this challenge, this paper introduces a groundbreaking blockchain-enabled evolutionary indirect feedback graph algorithm for the secure management of interconnected spatiotemporal datasets. The algorithm utilizes a generative neural network model for data imputation, predicting and generating plausible values to improve dataset completeness and integrity. The core architecture utilizes blockchain technology to optimize data retrieval efficiency and uphold robust access control mechanisms. The algorithm incorporates indirect feedback mechanisms, allowing users to provide implicit feedback through their interactions, enhancing the relevance and efficiency of data retrieval. In addition. sophisticated graph-based techniques are used to model intricate relationships between data entities, facilitating seamless data retrieval and sharing in interwoven datasets. The algorithm’s data security approach includes comprehensive access control mechanisms, encryption, and authentication mechanisms, safeguarding data confidentiality and integrity. Extensive evaluations show significant enhancements in retrieval performance and access control precision, making the proposed model a promising solution for the secure management of expansive interconnected spatiotemporal data.

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Author Biographies

Song Li, School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China

Song Li obtained a Master’s degree in computer application technology from Nanjing University of Aeronautics and Astronautics in China. At present, he is pursuing a Ph.D. degree in cyberspace security at Guilin University of Electronic Technology, also in China. His primary research interests revolve around data security and blockchain technology.

WenFen Liu, School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China

WenFen Liu earned a Ph.D. in cryptography from the Information Engineering University of the People’s Liberation Army in 1999. In 2017, she joined Guilin University of Electronic Technology as a distinguished faculty member. Currently, she holds the position of Doctoral Supervisor and serves as a Professor at the School of Computer and Information Security, Guilin University of Electronic Technology. Her research focuses primarily on privacy protection, statistical analysis technology for big data security, and the design and analysis of cryptographic algorithms.

Yan Wu, Unit 95795 of PLA, Guilin, China

Yan Wu obtained a Bachelor’s degree in Management from Guilin University of Electronic Technology in 2006, and now she works in the Unit 95795 of People’s Liberation Army. Her research interests mainly include big data and multimedia applications.

Jie Zhao, School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China

Jie Zhao received a B.Eng. in the School of Information and Management Science at Henan Agricultural University, Zhengzhou, China. He is currently studying for a M.Eng. in electronic science and Technology at the School of Computer and Information Security, Guilin University of Electronic Technology, China. His research interests focus on blockchain.

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Published

2024-03-27

How to Cite

Li, S., Liu, W., Wu, Y., & Zhao, J. (2024). Generative Architecture for Data Imputation in Secure Blockchain-enabled Spatiotemporal Data Management. Journal of Web Engineering, 23(01), 111–164. https://doi.org/10.13052/jwe1540-9589.2315

Issue

2024: Vol 23 Iss 1

Section

Advances, Risks, Solutions, and Ethics in Generative AI for Web Engineering