Data Asset Valuation and Security: A Distributed Machine Learning Approach

Abstract

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.