Population health data set sensitivity processing method and system

Abstract

The invention discloses a population health data set sensitivity processing method and system. The method comprises the steps of obtaining a to-be-evaluated population health data set; carrying out sensitive information identification on each feature of the population health data set to obtain sensitive features corresponding to each feature, wherein the features comprise a metadata feature, a data item feature and a data value feature; analyzing each sensitive feature to obtain an analysis result corresponding to each sensitive feature; calculating based on the analysis result corresponding to each sensitive feature to obtain a sensitivity comprehensive evaluation result of the population health data set; and generating a sensitivity evaluation report of the population health data set based on the sensitivity comprehensive evaluation result. According to the method, sensitive information discovery, recognition, analysis and processing are realized, application requirements of population health data set sensitivity evaluation is met through multi-dimensional analysis, and the efficiency and safety of subsequent population health data application are improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a sensitivity processing method and system for a population health data set. Background technique [0002] Population health data sharing plays an important role in improving the quality and effectiveness of medical care, assisting medical management decision-making, improving the level of medical research and the transparency of scientific research results, and controlling medical costs. However, population health data involves a large number of individual or group identity information and health information. If highly sensitive information such as genetic information and genetic information is leaked during the data sharing process, it will bring different degrees of security risks and property losses to the country, society or individuals. It can be seen that data sensitivity processing is an important basis and premise for population health data desensitization proces...

AI Technical Summary

Problems solved by technology

[0003] Existing research focuses on the identification and removal technologies and solutions of population health data sensitivity information. The current research on data sensitivity assessment methods is insufficient, and no effective assessment method system and a more comprehensive data sensitivity assessment process have been formed. There is a lack of induction and formation system. , Comprehensive population health data sensitive information category

Moreover, most of the existing sensitivity assessment studies focus on the measurement of sensitive information at the level of data records, attribute classes, attributes, and attribute values, and there are few studies on the overall sensitivity assessment at the dataset level.

Further, most of the existing research methods only calculate the sensitivity level or risk score, and the calculation process and results are not very interpretable, difficult to understand, and lack human-machine understandable forms

It can be seen that the existing population health data sensitivity processing cannot meet the actual population health data sensitivity assessment needs, which reduces the security and data processing efficiency of subsequent population health data sharing.

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