Data privacy is an increasing concern in today's world, especially for valuable information such as payment details or health records. As such, many people who work with that kind of content use data obfuscation, which is the process of making data hard to read or interpret except by the individuals who have permission to see it.

People frequently protect data in this way in testing environments. Relying on masked data allows them to build copied databases for testing with concealed sensitive information. That information offers the advantage of increased volume and valid data that databases from scratch don't. Here are six best practices for data obfuscation that remain mindful of security.

1. Use Irreversible Methods

Keeping data hidden is useless if the people who seize it could reverse the safeguarding mechanism and make the information visible. When choosing and implementing methods, professionals should always verify that those techniques offer complete protection, even if unauthorized parties try to reveal the information.

2. Ensure You Use a Repeatable Technique

You may hear this process of protecting data referred to as data masking, and you'll undoubtedly notice there are various methods to employ. One simple option is to replace meaningful information with non-valuable characters. For example, a phone number may appear as XXX-XXX-XXXX.

When people pick a method to conceal data, it's crucial to check that it produces the same results again and again when masking the same source data. If it doesn't, the technique is not reliable and may not function as intended when needed.

3. Understand the Difference Between Data Obfuscation vs. Encryption

Many people get confused about data obfuscation and encryption. They lump them together under a single definition and use the two terms interchangeably. But, the two are not the same. One difference is that encrypted data requires people to have a decryption tool to read it. More simply, data obfuscation makes it difficult to read data, and encryption makes the information scrambled to people who don't have the encryption key.

But, people who work with sensitive data often use obfuscation and encryption together. Data security professionals can think of them as both being useful for protecting data, although they serve different functions.

4. Determine Regulatory Requirements

One of the primary reasons why people depend on data masking is to protect data in non-production environments. For example, when people need data to test functionality or processes, obfuscated data ensures that private details stay concealed while individuals work with the information. It's crucial to figure out any regulations that dictate the responsible use of data.

Failing to follow the regulations set forth by a country where an enterprise operates could make them liable for receiving fines. In short, companies should not assume their method of obfuscating data is suitable before verifying that it aligns with all regulatory standards.

5. Choose Purpose-Driven Data Masking Methods

It's not sufficient to depend on the same method to obfuscate the data in every case. Always assess your projects and think about how to mask the data in ways that suit your needs. If unique data is a requirement, companies might use a technique called shuffling. It mixes up the values assigned to each entry in a data set.

For example, shuffling could scramble employee names/salary data. In that case, an employee has a salary value assigned to them, but not the actual amount. Or, a person could use the lookup substitution method. It includes a lookup table in the production environment that assigns aliases to real data.

6. Stay Abreast of New Options

It's imperative to keep data protected. Data breaches are getting more frequent than in the past, and they can have severe ramifications for the companies that experience them.

Even when companies find several methods that mask data that work well for them, it's smart to stay updated about emerging methods. For example, Google offers a new version of hiding information called differential privacy. It works in TensorFlow, Google's machine learning framework. Thanks to differential privacy, developers can develop artificial intelligence (AI) models while keeping data secure.

It's also possible to invest in tools that offer dynamic data masking. It safeguards data in real-time, thereby saving developers from taking extra steps during their testing process. Users have to set masking parameters first. Then, the tools send either cloaked or fake data to non-authorized viewers.

A Framework for Success

The information here should clarify data obfuscation vs. encryption, as well as provide tips for people who want to establish or improve practices for keeping data protected. Although it's necessary to keep company-specific needs in mind, the suggestions here should provide useful reminders for how to ensure that the right people view data at the proper times.

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About the Author

Kayla Matthews is a tech-loving blogger who writes and edits ProductivityBytes.com. Follow her on Twitter @productibytes to read all of her latest posts!