A zero-copy full mirroring system and method for large data static database desensitization

Abstract

The invention discloses a zero-copy full-mirror big data static database desensitization system, which mainly includes four modules: a system management module, a data source management module, a data desensitization task execution module and a desensitization configuration management module, wherein the data desensitization task The execution module is the core of the whole system. A zero-copy full-mirror big data static database desensitization system of the present invention includes a system management module for managing the basic functions of the system, a data source management module for managing the source database address and target address of the system, The configuration and execution of the desensitization task, the monitoring data desensitization task execution module, and the desensitization configuration management module that provides the configuration basis for the desensitization task. The invention also discloses a large data static database desensitization method with zero copy and full mirroring.

Description

technical field [0001] The invention relates to the cross technical field of computer technology and information security, in particular to a method and system for desensitizing a large data static database with zero copy and full mirroring. Background technique [0002] Today, the development of social informatization and networking has led to the explosive growth of data. With the rapid development of business in various industries, a large amount of sensitive data related to individuals, enterprises, and governments has accumulated in these business production systems. Direct use of real data of business systems in actual development, testing, outsourcing and other non-production environments can easily lead to leakage of sensitive data. Therefore, most users rely on data desensitization technology to deform sensitive information through desensitization rules to protect sensitive private data. [0003] At present, the data desensitization methods used by users mainly inc...

AI Technical Summary

Problems solved by technology

[0006] 1) When configuring desensitization rules through database commands, users need to have a good understanding of sensitive data in the database. Once users omit sensitive data, sensitive data will be leaked;

[0007] 2) When the number of tables and fields in the database is huge, users need to spend a lot of time configuring desensitization rules, which is time-consuming and labor-intensive;

[0008] 3) Setting desensitization rules based on database commands will occupy database resources and affect database performance

[0009] 4) In the development and testing environment, based on the database command method, the business relevance and consistency of the desensitized data cannot be maintained, which affects the normal use of the desensitized data

[0012] 1) Data desensitization based on data landing is to extract data from the production environment to the local. When the amount of data is relatively large, data extraction will seriously affect the performance of the database, and even affect the normal use of the user database;

[0013] 2) Data desensitization based on data landing stores the extracted production data locally, which increases the risk of data leakage. Once the storage device is stolen, it will cause huge losses to users;

[0014] 3) In the data desensitization method based on data landing, in order to improve the desensitization speed, relevant changes will be made to the user database when performing the database timing incremental desensitization task, which may affect the performance of the user database;

[0015] 4) The data desensitization method based on data landing only considers the timing incremental data desensitization of the entire database, but does not support table-level timing incremental desensitization and view-level timing incremental desensitization, which cannot meet user needs all scenes

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