Data Masking at a Glance

The more we associate data with our business processes, the more valuable it becomes and so risky to handle it securely. Health care records, personal information, credit card details are turning to be more and more vulnerable to hackers. One has to get rid of random access and copies of the essential data. In such situation, the issue one faces, at first, is how to protect data without disturbing the regular business processes and applications. The answer to this is Data Masking.
Data Masking replaces sensitive data with non-sensitive proxy in such a manner that the copies of the data seem to be an original one. Thus, we can use non-sensitive data without changing the supporting applications or data sensitive services. Mostly, data masking helps in limiting the propagation of relevant data within IT systems by distributing proxy data sets for testing and analysis. In other cases, it dynamically provides masked content when a user asks for sensitive information.

To better understand how Data masking works, have a look at the following model:

  • ETL: It stands for Extract, Transform and Load. It is the most commonly used masking approach. It works well in case of dedicated data masking platforms, data management tools with integrated masking and encryption libraries, and embedded database tools.
    To know more, check out meaning of each term used:
  1. Extract: One can select the whole database or few sub-set of data based on some selection criteria. It is   observed that the obtained data is often formatted and thus, easier to mask. On applying any query, the retrieved data is streamed directly to the data masking application such as a .csv or .tsv file. The data is then transferred to the masking platform as an encrypted file or over an encrypted SSL connection.
  2. Transform: In this step, data is masked. And sensitive information is transferred into a safe approximation of the original information.
  3. Load: In this last step, data is loaded into a destination database after applying masking. The masked data is copied to more than one destination database where it is loaded back into tables.
  • In-place Masking: Many a time, we need a masked copy within a database. It is used because it gives us a chance to take benefit of database’s facility with management and manipulation of data. They offer very high masking performance and incredibly adept at data transformation. It can be used for partial or iterative modifications to existing data files also.
  • Dynamic Masking: Dynamic Masking is a superior method to secure data in Web application environments where users cannot be authorized correctly, without costly recoding of applications. It is of two types: View-based dynamic masking and Proxy-based dynamic masking.
  1. View-based masking: This type of masking makes it easier to test production applications in a production environment by allowing masked data only to less-privileged users. This model is suitable for companies with large production databases, and those which are not afraid of extra overhead on production database servers.
  2. Proxy-based masking: It is different from View-based masking as it operates outside the data repository and is available for non-relational systems only.

Use the above and secure your data. And thus, increase the productivity of your business.

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