The database desensitization function of Andang KDPS data protection system is a security strategy that can desensitize sensitive data in the database so that sensitive information will not be leaked when the data is accessed. This function mainly has the following characteristics:
- Scalability: It can support multiple database types, such as Oracle, MySQL, SQL Server, etc., and desensitization rules and strategies can be customized to adapt to different business scenarios and data security requirements.
- High performance: Using efficient data desensitization algorithms and optimization technologies, data desensitization processing and data conversion can be completed quickly without affecting the performance and response speed of the system.
- Data security: During the data desensitization process, the system will automatically encrypt sensitive data to ensure data security and confidentiality. At the same time, the system also supports custom desensitization rules and strategies to avoid misoperation and accidental leakage of sensitive data.
- Automated management: The system provides automated management tools and interfaces to facilitate the formulation, management and monitoring of data desensitization strategies. At the same time, it also supports backup and recovery of desensitized data to ensure data security and integrity.
The database desensitization function of Andang KDPS data protection system is a very practical security strategy that can effectively protect sensitive data in the database, prevent data leakage and abuse, and improve the data security and competitiveness of enterprises.
Database static desensitization and dynamic desensitization are two different data desensitization methods, and their purposes and application scenarios are different.
Static desensitization is mainly used in non-production environments. It is a process of desensitization after data extraction. In this process, sensitive data is converted into a non-sensitive form, but it can still meet the data needs in scenarios such as testing, development, training, and data analysis. The implementation principle of this method is mainly to automatically identify sensitive data through built-in rules, and then use desensitization algorithms to bleach the data. Static desensitization can provide different loading methods such as file to file, file to database, database to database, database to file, etc. The exported data is stored in an external storage medium in a desensitized form.
Dynamic desensitization is mainly used in production environments. It is the process of desensitizing sensitive data when it is accessed by low-privileged individuals. The implementation principle of this method is to accurately parse the SQL statement to match the desensitization conditions, and then rewrite the query SQL or intercept and protect the desensitized data to the application after successful matching. Dynamic desensitization can not only dynamically desensitize different query conditions, but also dynamically desensitize different data types, so it can protect data security more flexibly.
Generally speaking, static desensitization and dynamic desensitization have their own characteristics, and you need to choose which method to use based on specific data processing needs and application scenarios.
Example reference
//FF2 encryption
String text = "2151142362";
//alphabet should include the plaintext range and the ciphertext output range.
String alphabet = "0123456789";
//FF1 encryption
String ciphertext = client.fpeEncrypt(text, Fpe.FF1,alphabet , 16, "keyLabel");
//FF1 decryption
String text = client.fpeDecrypt(ciphertext, Fpe.FF1, alphabet , 16, "keyLabel")
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