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!