Data masking feature is very much in demand by business organizations to secure and protect the business critical data, another reason for data masking is to suffice data assets security requirement required by compliance and regulatory authorities. Data masking is basically enablement of a security feature on a specific column data inside a table in database, data is virtually hidden from the end users. When user issues a select query, he will get data from all columns excluding masked column, query will generate a junk data most probably in unreadable format. Today variety of data is exchanged between organisation and its outside business partners; therefor it’s all the more important to mask the necessary data element which is accessed by internal and external users. Data element accessed by outside users is prone to be attacked by hackers or cyber criminals. Such malpractice can cause into loss of revenues and trustworthiness of data. These days all the databases coming with the data making feature from all industry standard database vendors.
Data Masking Methods
Colum Encryption
In this technique, a key is provided by data provider agency, key is asked when data has to be decrypted, if data receiver is unable to provide the appropriate key or lost the data recordsets unable to open and locked on multiple unsuccessful try.
Data element Substitution
In this technique, a randomly used character or numbers put up against a column values by replacing the original column contents, however it’s also looks similar to the original column contents. For example, the product category in a product database could be substituted by commonly used product portfolios column contents.
Rows Shuffling
In this technique data content in table columns are changed frequently, data is shuffled and aligned in such a way that select query results cannot establish a meaningful data relationship to the end users.
Number and Date columns Variance
This technique is useful for numeric or date columns, in this technique an algorithm needs to write to a numeric or date value column in a column by some random percentage of its real value. For example closing stock might have random variance of ±15% placed on it.
