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Introduction To Data Masking For Enabling Security And Safety

Introduction To Data Masking For Enabling Security And Safety 05 Dec,2019

For all enterprises, these days test data privacy is critical and therefore Data Masking is the only and best way to deal with security.

It basically ensures that only those people can see the data who really need to see it and ensuring the appropriate time for it.

What is Data Masking?

 

Data Masking refers to a process of hiding original data with modified content. It is basically a process of creating a similarly structured but inauthentic version of any organization’s data that can be used for purposes such as software testing and user training.

In data masking, data format remains the same, but we change the values. Altering the data in a number of ways, including encryption, character shuffling, and word substitution. This is done basically taking any method into consideration just to ensure the values should get changed that makes detection and reverse engineering impossible.

 

Benefits of Data Masking

 

Data masking is essential in almost all regulatory industries. Personal identifiable information needs to be protected from overexposure. Hence, by masking data, an organization exposes a certain amount of data only to their testers or database administrators which therefore gives reduced security risk.

 

Various Techniques used for Data Masking:

 

  1. Substitution

This is one of the most effective and reliable methods, as the authentic look and feel are preserved for the records in this method.

In this method, another authentic-looking value is substituted with the existing value. Across the world, this method is applied in various DB structures like ZIP codes, postcodes, telephone numbers, and various social security numbers. These substitution files are needed to be fairly extensive so having large datasets for substitution and ability to apply those customized data substitution sets should have been a key element of the evaluation criteria for entering any data mask solution.

Usage Instructions:

  • Purpose: To mark such values that are a combination of Alpha and numeric characters that are not formed under any basic rule.
  • Algorithm: It basically shows the variation of preserving any Referential integrity.
  • Description: Mask dictionary substitution component basically uses a customized dictionary defined by the User for Substituting values. It basically preserves referential integrity while preserving referential integrity.

 

   2. Shuffling

Shuffling is basically a form of Data Obfuscation. It basically derives the substitution set from the same column of data that is being masked. We randomly shuffle the available data here within the same column.

The only drawback we can look upon here is if we use it in isolation and if anyone with the knowledge of original data tries to apply a “What if” scenario to that data set can pick back together with a real identity. This method is open to being reversed if a Shuffling algorithm is deciphered or converted.

But with the intimate knowledge of original data anyone could derive a true data record back to its original values.

   3. Number and Data Variance

This method is useful in financial and data-driven information fields. This masking technique can leave a meaningful range in financial data such as payroll. If the variance provided is as less as 10 percent, even then it’s still very meaningful data.

If executed properly, this technique can provide us a useful set of data without losing up important financial information or transaction dates.

For example: A record that offers employee salaries can give you the range in those salaries between the highest and lowest paid employees when masked. Accuracy of this technique can be measured by applying the same variance to all salaries in the set.

   4. Encryption

It is basically one of the most complex approaches for solving data masking issues. When data is encrypted the authorized users can access it with a key. This is also a secure type of Data masking. Data here is masked with an encrypted algorithm.

The major issue of data encryption while preserving the properties of entities got real recognition and newly acquired interests among various people. This led to the design of a new algorithm called Format preserving encryption (FPE).

   5. Nulling Out or Deletion

This is one of the simplest approaches to apply null value to a particular field. It basically prevents the visibility of data elements. In simple words, data value becomes null to anyone who is not authorized to access data.

   6. Masking Out

It is one of the most effective methods to prevent data from being viewed. Keeping the data real and not fully masking it all together makes a great emphasis. The most common examples for this is Credit card information while billing where the last 4 digits are kept real and whole data is encrypted with an (X) sign. For example: XXXX XXXX XXXX 8975.

Different Types of Data Masking

 

   1. Static Data Masking

It is a process in which data is usually masked in the original database environment. Content here is copied or duplicated in the software testing environment which is then further shared with third-party vendors.

Taking into consideration the environment of DB, production DBA will typically load table backups to a separate environment, reducing the dataset to a subset that holds data for a particular round of software testing, apply data masking rules, apply necessary changes in the code from source control, pushing data to the desired environment.

   2. On the Fly Data Masking

In this process transferring data occurs from the environment to the environment without data touching the disk on its way. For heavily integrated applications and environments that do continuous deployment, this type of masking is most effective. An extract transform load (ETL) process occurs in this type of data masking where data is masked within the memory of a given database application. It is most effective in agile companies where we have to make continuous deliveries.

   3. Dynamic Data Masking

Data is secured through automation or IT Department in real-time. It never leaves the production database and hence it is less susceptible to threats.

Contents are jumbled in real-time, making it inauthentic. Hence data is not exposed to those who excess the database. Certain type of sensitive data is masked using a reverse proxy through a dynamic masking tool called Resource.

   4. Data masking and Cloud

These days now Organizations develop new applications in the cloud quite often. This is done regardless of the fact that whether the application will be hosted in the cloud or on the premises.

It allows organizations to use Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Data masking has now become an important part of these processes.

List Of Some Best Data Masking Tools

 

   1. DATPROF – Test Data Simplified

It provides a smart way of masking and generating data for testing the database. This is used to handle the complex data with an easy interface

   2. Oracle Data Masking and Subsetting

Oracle Data Masking and Subsetting benefits helps in removing the duplicates for testing data, development, and other actions by removing redundant data and files. It supports non-oracle databases and takes less time to run.

   3. Hush-Hush

It helps in identify data against internal risk. Its main features are easy installation, robustness, takes less time to create workflows and speed up development.

   4. NextLabs Data Masking

It offers established software that can shield data and guarantee compliance in the cross-platform. It is Dynamic Authorization technology with Attribute-Based Access Control. It secures all the critical business data and applications.

   5. IRI – FieldShield

FieldShield is popular in the DB data masking and test data market due to its high speed, low cost, and range of supported data sources. It’s main feature is Multi-source data profiling, discovery (search) and classification. It provides a high performance, without the need for a central server.

Pooja Guleria Pooja Guleria works on bugraptors as Senior QA Engineer. she is having 5 years experience in web & mobile application testing. Comprehensive work experience in Real Estate, She is expertise in QA practices Project Management, Team Management, Client Relationship Management. Participating in Release Planning, Product Backlog, Product Reviews, Defect review, Root cause analysis.

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