A method and system for quantifying the risks of data circulation in a trusted data space

By employing multi-dimensional risk perception and dynamic quantitative assessment, combined with adaptive strategy adjustments, the problem of inaccurate risk assessment in the trusted data space has been solved, enabling precise measurement and intelligent control of data circulation risks, and improving the security and efficiency of data circulation.

CN122133176APending Publication Date: 2026-06-02YUANBAO TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YUANBAO TECH
Filing Date
2026-01-30
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing trusted data space technologies suffer from several problems in quantifying data circulation risks, including a single risk measurement dimension, static assessment lacking adaptability, disconnect between risk and control strategies, and opaque quantitative models. These issues lead to inaccurate data circulation risk assessments and make dynamic management difficult.

Method used

By employing multi-dimensional risk perception, dynamic quantitative assessment, and adaptive strategy adjustment, the system generates an overall risk value for data circulation through multi-dimensional risk data collection, standardized processing, and comprehensive calculation. Based on the risk level, it generates differentiated control strategies and achieves dynamic adjustment by combining adaptive algorithms and smart contracts.

Benefits of technology

It enables precise measurement and intelligent control of data circulation risks, improves the comprehensiveness and real-time nature of risk assessment, reduces reliance on human experts, and enhances the operational efficiency of the trusted data space and the trustworthiness of data providers.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention provides a method and system for quantifying data circulation risks in a trusted data space. The method includes: Step 1: Multi-dimensional risk data collection and feature extraction; Step 2: Converting the collected multi-dimensional risk data into computable standardized risk factors, and quantifying and normalizing these risk factors; Step 3: Comprehensively calculating the standardized risk factors to generate an overall risk value for data circulation, and classifying risk levels based on the overall risk value; Step 4: Generating differentiated data circulation control strategies based on the classified risk levels. This invention's method, through multi-dimensional risk perception and dynamic fusion assessment, can identify more potential risk scenarios compared to traditional single-dimensional assessment methods. By reducing the reliance on human experts in risk assessment, it significantly improves the operational efficiency of the trusted data space.
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