Data masking is a vital tool for constructing a framework similar to the available one but has an inauthentic modification of the organization’s details that can be used for different purposes, such as customer training and product testing. But the main goal is to preserve the original data if the actual data is not required by providing an operating replacement for different scenarios. In this article, the approaches, benefits, implementations, and drawbacks are discussed. Although most businesses use strict encryption mechanisms to save output records, it is possible to use less accurate data for operations.
Why do we Need Data Masking?
If the tasks are outsourced, and the organization has minimum control over the conditions, the dilemma is solved. In terms of enforcement, most companies are not able to disclose the real details now. The data masking method is then implemented to resolve the above problems. The format of the data is not altered here, only its values are altered. Data can be modified in different ways, such as character shuffling, word replacement, character replacement, encryption. The value can be changed in some way that makes reverse engineering or tracking difficult, regardless of methodology. The manufacturers of data masking materials are Oracle, Informatica, Compuware, IBM, and Dataguise.
How Data Masking is Carried Out?
The following restrictions should be added to compiling critical data components:
- The data output should denote the source data, and it is essential to use it efficiently to validate and improve the data by representing it. The masked data should be permanent, and the source data from the masked data should not be generated as an initial. It should be known that it is not appropriate to mask the details.
- Both data elements should not be masked. Only sensitive data should first be masked and non-sensitive data masked if any re-creation of sensitive data is needed. The output step is replicated, and the same source data is continuously masked to produce the same output using the same masking technique. Substantial integrity has to be upheld, and it should ensure that the masked data should be reusable.
- To save the context and format of the data elements, specific masking approaches are used since they should be reliable, relevant, repeating, and used efficiently.
- Substitution is a method that substitutes other meaningful data for the sum of confidential data. The postal code can, for example, be replaced at random by a count of valid postal codes.
- Non-meaningful values are replaced by spacing, nullifying, and masking. Any data was substituted for the importance of social security. The encrypted keys include other instances where space is conveniently substituted for the data.
- Data variance and numbers provide modifying each value or date by entering the actual values with specific random values. By preserving the distribution and variety of values, it offers efficient obfuscation. For e.g., an employee’s pay is entered in an upward or downward direction by 15 to 20 percentages. Similarly, date of birth with a period of 45 days after or before the real date of birth may be reached.
- Format preservation encryption is an algorithm that provides repeated values to preserve the original format. By using an appropriate decryption method, the real value may be retrieved. The decision to use a good algorithm is based on the enterprise’s data protection policies. With 128 advanced encryption standard algorithms with a 24-byte triple data encryption algorithm, the encryption methods can be restricted, and the protected hashing method is used to protect the 24-byte key.
Advantages & Disadvantages of Data Masking
Below are the advantages and disadvantages:
- The substitution retains the feeling and looks at the new data, but it becomes frustrating and difficult to locate the correct details to replace when working with higher dimensional data.
- The shuffling preserves the data presentation and controls higher dimensional data efficiently, but it is inefficient when processing minimum size data. The original data is not interrupted and can be transformed into unshuffled if the algorithm is not adequate.
- By retaining the distribution values, the data variance and number function well on the numerical data and only apply to the numerical values.
- Encryption is an efficient data masking tool, but it affects the formatting in the appearance of the information. Seeing the data as encrypted is easy. The encryption can be broken with enough effort, and any third party can readily access the data.
Planning and applying data masking tools is the most comfortable and most reliable solution for data masking. The method of data masking means a simple structure of the dynamic masking process and a perfect solution for the database’s security. The database protection and data sunrise package suite is designed with static and dynamic demands for data masking.