Data privacy is of utmost importance in this age wherein the influx of information seems to be as fast as the speed of light. Organizations and individuals are exerting efforts in maintaining their privacy without sacrificing the quality of data being passed on from one source to another. Aside from data de-identification software, data masking software has also been created to cater to the needs of the world of information technology (IT).
First, let us define “data masking”-it is the process of obscuring particular elements of info within a data store. This ensures that sensitive data will be replaced with realistic data, and by this we mean data that does not really exist. The primary purpose of data masking is to make sensitive information inaccessible and unavailable outside of the authorized milieu. This process is usually done to provide copies of data to support development and test processes without exposing sensitive information and avoid leaking. In addition, masking algorithms are made to be repeatable to maintain referential integrity.
For data masking to be effective, data must be altered in such a way that the real actual values are impossible to be re-engineered or determined. Since the functional appearance of the info is maintained, the user can still test it. The data can also be encrypted and decrypted while security policies are established. The separation of duties between administration and security is also instituted.
You can perform data masking by using an array of techniques that includes the following:
Shuffling – This uses the existing data as the substitution dataset and moves the values in such a way that no value appears in its original row.
Substitution – A technique that replaces existing data with random values from previously prepared datasets.
Encryption – Scrambles data algorithmically and this technique does not leave the data appearing to be realistic. Also, encryption tends to make the data bigger.
Nulling out or deletion – This technique simply removes the sensitive data through deletion.
Number and date variance – Varies the existing values in a specific range to disguise them.
Masking tool possesses some key features to achieve the following results and goals:To create realistic test data that ensure the appearance of unusual patterns in data testing
Caters to the needs of health and clinical databasesDeals with massive databases in a continuous mode to refresh test data cycles rapidlyTo save masking function specifications to be used on other databases at scheduled intervalsRetains referential integrity across tables to guarantee that patient information is always maskedEnsures that adversaries will not be able to reverse engineer data maskingProcesses large amounts of data rapidlyMasks keys to retain the same size and field type to maintain referential integrityIncludes reference databases and templates for the most common direct identifiersIt is important to know that masking is not enough if you really want to protect the data source’s privacy. You also need to use de-identification software to produce a very powerful combination of tools. With these two, you can quantitatively demonstrate the reduction of re-identification risk and guarantee maximum info utility for output data.
Read more about big data de-identification software and privacy analytics at PrivacyAnalytics.ca
