Data desensitization method and device

Abstract

The invention provides a data desensitization method and device. The method comprises the following steps of: receiving to-be-desensitized original data in real time; respectively desensitizing the original data by adoption of a first desensitization algorithm and a second desensitization algorithm so as to obtain first desensitized data and second desensitized data, and obtaining the first desensitized data and the second desensitized data in to-be-analyzed data; and if second encrypted data of the second desensitized data is same as preset second encrypted data, analyzing the second desensitized data and returning an analysis result, wherein the analysis result is the original data.

Description

technical field [0001] The present application relates to the technical field of data desensitization, and in particular to a data desensitization method, device, computing device and storage medium. Background technique [0002] In the prior art, some sensitive data needs to be desensitized when the client performs data display. At present, common desensitization methods include static desensitization and ordinary dynamic desensitization. Static desensitization is simple and efficient, and basically has no impact on business relevance, but it cannot be quickly changed to adapt to the business. Most usage scenarios are static data warehouses and analysis systems (OLAP) applications cannot desensitize sensitive data in real time, and need to add desensitized data columns while desensitizing data, which has a great impact on the code; in addition, it is not friendly to large data columns, for example: There are 2 billion data in this column, so if you use static masking, you ...

AI Technical Summary

Problems solved by technology

At present, common desensitization methods include static desensitization and ordinary dynamic desensitization. Static desensitization is simple and efficient, and basically has no impact on business relevance, but it cannot be quickly changed to adapt to the business. Most usage scenarios are static data warehouses and analysis systems (OLAP) applications cannot desensitize sensitive data in real time, and need to add desensitized data columns while desensitizing data, which has a great impact on the code; in addition, it is not friendly to large data columns, for example: There are 2 billion data in this column, so if static masking is used, this column needs to be completely masked before it can be used

Ordinary dynamic desensitization In the process of data use, the data is blurred before the data enters the scene that needs desensitization to become fuzzy data. For example: 123456 becomes 12***6 after ordinary dynamic desensitization; Sensitized data will lose data relevance and cannot be used for subsequent business processing

[0003] At present, in many scenarios, there will be different data sources from relational database management system (mySQL) or Lucene-based search server (elasticsearch) at the same time, and the data storage is not stable; the client access server accesses internal services through proxy, If the original data is called directly, it is easy for sensitive data to be stolen by hackers, etc., causing data security risks

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