Enhancing knowledge accessibility in digital economy with search over enterprise data

Kargar, Mehdi

doi:10.1007/s00779-022-01668-y

Cited by 1 publication

(1 citation statement)

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“…This is also very important in internal risk assessment. For digital economy platforms, there are also risks to the behavior of internal employees [12][13]. Traditional risk assessment methods usually only consider specific behavioral indicators of employees and cannot fully assess their risk level.…”

Section: Multi Factor Evaluation Algorithmmentioning

confidence: 99%

Multi Factor Risk Assessment Algorithm in Digital Economy Platform

Zhao,

Cao,

Mei

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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With the rapid development of the digital economy, the platform economy has become an important component of today's society. A digital economy platform refers to a form of economic activity that conducts transactions and provides services through online platforms. The platform economy has advantages such as efficiency, convenience, and openness, but it also faces risks such as data security, user privacy, and market competition. Therefore, how to effectively evaluate and manage risks in the platform economy has become an important issue in the field of digital economy. This article aimed to explore the application of multi factor risk assessment algorithms in digital economy platforms, in order to improve the risk management level of the platform and promote the healthy development of the platform economy. This article conducted research on user risk, internal risk, transaction risk assessment, and data security risk assessment. Through algorithm comparison and investigation, relevant data on internet digital platforms were obtained and the performance of evaluation algorithms was evaluated. Experimental data showed that the Relief algorithm performs best in enterprise competition risk classification, with an accuracy of 80% in data security, 79% in user trust, 84% in legal and regulatory accuracy, and 85% in talent and technology accuracy.

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Section: Multi Factor Evaluation Algorithmmentioning

confidence: 99%