SPEAKER: Business is booming at Kitty Catwalks.
And meow, with more customers and employees,
they've got their paws on a whole lot more sensitive data.
They know that Google Cloud DLP will
help them protect that data and reduce their risk.
But with so many transformation options,
it could be a real challenge figuring out which ones to use
and when.
Today, we'll talk about what de-identification
is, the factors to consider when choosing
a de-identification technique, and check out
a few examples, including crypto-based tokenization.
De-identification is the process of removing
personal identifying information from your data.
Common examples of personal identifying information,
or PII, are credit card numbers, addresses, phone numbers,
and names.
Of course, some parts of your system
might need access to this information,
billing for example.
But other parts do not.
This is where de-identification with DLP comes in.
The DLP API can intelligently detect sensitive information
and use a number of de-identification
transformation techniques to mask,
delete, tokenize, or otherwise obscure the data.
Choosing the right de-identification
transformation will depend largely on two things.
First of all, we're going to need to consider what kind
of data we want to de-identify.
Are we going to try to de-identify dates?
By using date shifting, we can shift date values
by a random amount of time.
Or maybe we're trying to hide text from images.
Well, image redaction will work there.
DLP uses info type detectors and optical character recognition
to find text in image files and mask it
with an opaque rectangle.
The second thing to consider is the purpose
for de-identifying data.
This is important, because some de-identification techniques
will completely hide sensitive values,
while others will still allow you
to extrapolate important information about the data.
Let's consider an example.
Check out this excerpt from a chat log
between a customer and a Kitty Catwalks customer service rep.
When we de-identify it using masking,
the sensitive content is replaced
by a masking character, like an asterisk or a hash symbol.
There isn't much that we can deduce about the information
that's being removed.
When it's de-identified using replacement,
where it can be replaced by something else,
like its corresponding info type,
we can at least know that what the customer entered
was an email address.
In both cases, we successfully removed
the personal information.
But with replacement, we can at least
tell what info type was removed.
Other techniques use bucketing to help you generalize
distinguishing values in your data,
based on custom values or ranges.
This is especially useful for data
meant to be used for reports or for data analysis.
We'll be able to make use of the data
without exposing it unnecessarily.
For example, when the finance department
wants to write a report on salary info,
considering there's only one director of Cat Naps,
it'd be easy for anyone looking at this data
to learn what that person's salary is.
Potentially sensitive information.
By applying bucketing here, we're
able to use the financial data while protecting the employee's
privacy.
Another powerful de-identification category
worth noting is crypto-based tokenization.
Cloud DLP can securely encrypt sensitive data
using a cryptographic key and replace it
with a hash or a token.
Cloud DLP supports several types of tokenization,
including some that are reversible,
so that you can even re-identify the de-identified data.
Here's one more example.
We scanned some log files of Kitty Catwalks' issue ticketing
system and exported the results to BigQuery.
You'll see that, in the data column,
it shows text that we've had replacement
applied on various info types.
But check out the first column.
Here, we apply tokenization and [INAUDIBLE] the user ID.
The values in this column are unrecognizable.
But if we run a query like this one
to give us account based on the user ID,
we can see how many issues there are for each unique user.
This is accomplished without exposing the value that we've
replaced, so it can be used for data analysis
without risk of compromising user data.
Furthermore, the user ID values can
be re-identified by individuals or teams that
are authorized to access the encryption key used to tokenize
the values in the first place.
So which de-identification technique
is the best choice for Kitty Catwalks
to protect their sensitive customer and employee data?
The good news is cloud DLP offers
enough de-identification options and flexibility
that they won't have to prescribe a blanket
policy for all their PII.
Understanding the trade-offs of each technique
will empower them to get the most value from their data,
while protecting it every step of the way.
Next time, we'll take a closer look
and examine how some of these de-identification
transformations work to protect and secure your sensitive data.
See you then.
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