A method and system for risk assessment of sensitive information in SQL query result sets based on machine learning

Through machine learning-based methods, the sensitive information characteristics in SQL query results are parsed and evaluated, and the problem of difficult to detect and process potential sensitive information in the prior art is solved, achieving more efficient data security and privacy protection.

CN119760771APending Publication Date: 2025-04-04BEIJING XINSHU TECH CO LTD
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Patent Information

Application Number
CN202411822605.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-12
Publication Date
2025-04-04

AI Technical Summary

Technical Problem

The prior art is difficult to effectively detect and process potentially sensitive information in SQL query results, especially in unknown patterns and complex data scenarios, and it is difficult to ensure data security and privacy protection.

Method used

Using a machine learning-based method, through SQL query parsing, feature extraction, threat scoring and risk decision-making, we identify and evaluate the sensitive information characteristics in the SQL query result set, dynamically adapt to the characteristics of different data sets, and realize in-depth mining and processing of potential threats.

Benefits of technology

It significantly improves data security and privacy protection capabilities, can efficiently identify and process complex and sensitive information patterns, reduce the risk of potential data leakage, and achieve comprehensive protection of SQL query results.

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Abstract

The present invention proposes a method and system for risk assessment of sensitive information in a SQL query result set based on machine learning, which can efficiently identify and process potential sensitive information in a query result set, and effectively improve data security and privacy protection capabilities. By introducing a feature extraction module and a threat scoring module, it can dynamically adapt to the diverse features of different data sets, accurately capture complex sensitive information patterns, and achieve in-depth mining of potential threats. At the same time, the threat scoring module in the system adopts a sensitive information feature quantification method, and establishes a comprehensive threat scoring function based on the relevance and sensitivity of the data to evaluate the universality of the features and their sensitivity in the context. The present invention quantifies the risk level of sensitive information in the SQL query result set by calculating the probability distribution model and comprehensive scoring of the result set. According to the scoring results, the risk decision module can flexibly select processing measures. Through the dynamic adaptability and precise analysis method of the system, not only can more complex sensitive information be identified, but also the risk of potential data leakage can be effectively reduced, and comprehensive protection of SQL query results can be achieved.
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