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Data Asset Valuation and Security: A Distributed Machine Learning Approach

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Udgivet i:International Journal of E-Collaboration vol. 21, no. 1 (2025), p. 1-25
Hovedforfatter: Yang, Ling
Andre forfattere: Zhang, Yihong, Feng, Ye, Xue, Jun, Dong, Danhuang, Luo, Zhejun
Udgivet:
IGI Global
Fag:
Data processing
Experiments
Security
Classification
Competition
Internet
Machine learning
Energy consumption
Companies
Trade
Data quality
Assets
Electronic commerce
Competitiveness
Valuation
Quality assessment
Business competition
Innovations
Morality
Image classification
Data integrity
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Resumen:With the development of Internet technology, the amount of enterprise data has surged and become a core asset. In e-commerce, data security not only protects data, but also affects the ability of transforming data into assets, thus enhancing the competitiveness of enterprises. This study proposes a system framework based on distributed machine learning to enhance data integrity and network security. The system integrates advanced algorithms, effectively processes large-scale data sets, and performs well in cloud image classification tasks. Experiments show that the energy consumption of the system is reduced by 68.92% and the task processing speed is increased by 4.67 times. In addition, the data quality evaluation model is studied to provide decision support for e-commerce enterprises. These innovations provide a new perspective for data asset evaluation and security and also support the application of distributed machine learning in e-commerce.
ISSN:1548-3673
1548-3681
DOI:10.4018/IJeC.392027
Fuente:ABI/INFORM Global