Static data desensitization (SDM) is generally used in non-production environment. Sensitive data is desensitized from production environment and then used in non-production environment. It is generally used to solve the problem that the test and development library needs the correlation of data in the production library for troubleshooting or data analysis, but sensitive data can not be stored in non-production environment.
Dynamic data desensitization (DDM) is generally used in production environment to desensitize when accessing sensitive data. Generally, it is used to solve the problem that different degrees of desensitization are needed when reading the same sensitive data according to different situations in production environment.
Extended data:
According to the data properties of columns, data columns can usually be divided into the following types:
Columns that can accurately locate a person are called identifiable columns, such as ID number, address and name.
A single column cannot locate a person, but multiple columns of information can be used to potentially identify a person. These columns are called semi-identification columns, such as postal code, birthday and gender. A research paper in the United States pointed out that 87% of Americans can identify themselves only by using postal code, birthday and gender information.
Columns containing user-sensitive information, such as transaction amount, illness and income.
Other columns that do not contain user-sensitive information.
The so-called avoiding the disclosure of private data refers to people who avoid using data (data analysts, BI engineers, etc.). ) from identifying a row of data as someone's information. Data desensitization technology desensitizes data, such as deleting identified columns and converting semi-identified columns.
So that data users can analyze the data in # 2 (converted) semi-recognition column, # 3 sensitive information column and # 4 other columns, and to some extent ensure that users cannot be identified according to the data, thus achieving the balance between ensuring data security and maximizing data value.
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