Ever since the advent of private and sensitive information, there have been attackers that have tried to reach that data and use it for malicious purposes.
Many data masking software have been developed to make the task of accessing sensitive data ineffective. Each of the techniques has their strengths and weaknesses with each technique typically being best applied to a specific data type.
Following are the key data masking tools:
It refers to randomly substituting the contents of a column of data with entirely unrelated data, but the data looks identical to original data. Substitution is one of the most successful techniques for applying data masking and protecting the authentic sense of the data records. There are quite a few data field types where this tool provides optimal benefit in covering the whole data subset. In the majority of cases, the substitution files will need to be moderately wide so having large substitution datasets as well the capability to apply tailored data substitution sets should be an essential factor of the estimation criterion for any data masking solution.
The shuffling technique is a very simple form of data obfuscation. It is almost same as the substitution technique, but it obtains the substitution set from the very column of data that is being masked. In short, the data is shuffled randomly inside the column. Though if used in separation, anyone who is familiar with the original data can then apply a “What If” set-up to the data set and then reverse back together a real identity.
- Number and Date variance
The numerical variance technique is very helpful for applying to fields that contain monetary and date driven information. A method utilizing this mode of masking can still leave a significant range in a financial data set.
Encryption is the most sophisticated method of solving the data masking problem. The encryption algorithm requires that a “key” be applied to access the data by user rights. This sound like the best way out but in practice the key may then be given out to employees without the rights to access the data, and this then beats the idea of the masking implementation.
- Nulling Out/Deletion
Applying a null value to a particular area is the simplest approach to data. It is only helpful to prevent visibility of the data element. In majority cases, it reduces the level of data integrity that is preserved in the masked data set. It is not a realistic value and may fail any application logic validation that has been applied to the front end software in the system under test.
- Masking out
Masking out of some fields is another simple yet very useful technique of protecting sensitive information to be accessed. It is an extension of the prior method of nulling out, but there is greater importance in keeping the data real and not completely masked all at once.