In this lesson,
we will learn about data handling and management.
Data handling and management is used
to safeguard data throughout its entire lifecycle
to ensure its confidentiality, integrity, and availability.
Data handling and management concepts include
Data Anonymization and Data Sanitization.
Data Anonymization modifies data
to prevent the identification of individual data owners
while maintaining its ability to be analyzed.
Data Sanitization, on the other hand,
is the process of securely destroying data
to ensure that sensitive information is completely removed
before the storage media is disposed of or repurposed.
Let's learn more about Data Anonymization
and Data Sanitization.
First, we have Data Anonymization.
When dealing with data security,
it's important to protect sensitive information
collected from your users or customers.
One way to achieve this is through Data Anonymization,
which removes the ability
to uniquely identify individuals in data
while still allowing the data to be used for analysis.
Data Anonymization, also called de-identification,
involves stripping out identifying details from the data
before sharing it within or outside of your organization.
For example, if you track how many students pass or fail
a certification exam,
you only need the overall results for analysis,
not individual student names.
In this way, Data Anonymization helps you gather insights
without compromising anyone's privacy.
So Data Anonymization makes sensitive data
usable for other purposes
by removing personal information associated with it.
Common techniques for Data Anonymization include
data masking, tokenization,
as well as aggregation and banding.
Data masking replaces real data
with generic or placeholder values,
preserving the original data's format.
For example, substituting actual credit card numbers
with a series of placeholder digits protects the data
while maintaining its structure,
ensuring sensitive information is hidden
when accessed without a valid reason.
Data masking is not reversible.
Tokenization involves replacing sensitive data
with unique tokens that reference the original data
stored somewhere else.
Unlike other methods, tokenization is reversible,
allowing the re-identification of data
when needed for specific business purposes.
For instance, using random IDs for social security numbers
keeps the data safe, but accessible when necessary,
because those random IDs would be mapped
to the original social security numbers.
Next, aggregation and banding involve grouping data
to generalize individual information,
making it difficult to identify specific people.
This technique is often used in studies
where results are reported for groups
rather than individuals.
For example, consider a study on employee salaries
at a company.
Instead of listing each employee's exact salary,
the data could be grouped into ranges
such as $30,000 to $40,000 per year,
or $40,000 to $50,000 per year, and so on.
By reporting that 20 employees earn
between $40,000 and $50,000 per year,
the study hides individual salaries within a broader group.
This makes it impossible
to identify any one person's exact income,
protecting individual privacy,
while still providing useful data insights.
In the end, Data Anonymization is not foolproof,
and improperly anonymized data sets
can still be re-identified.
Here's a real-world example of some poor Data Anonymization.
I worked at a company that conducted an employee survey
to get some honest feedback about the company.
They made the survey anonymous by not asking for names,
or any directly identifiable information,
hoping that employees would feel comfortable
sharing their thoughts.
The survey included questions like
how they felt about their job, their pay,
and their overall job satisfaction,
along with a few demographic questions
like age, gender, and marital status.
Initially, it seemed harmless,
and the company assumed that data would stay anonymous,
but after reviewing the results,
they noticed a few good ratings and one really bad one,
a one-star rating with a comment saying
the CEO was terrible.
Curious about who left the comment,
the CEO looked at the demographic data.
The person was a married woman between 35 and 40 years old.
In a large company, this could mean dozens of people,
but this company was small,
and only one employee fit that description.
It was the CEO's wife.
She often joked around in surveys
knowing she would not get fired.
The lesson here is that even with aggregation and banding,
small groups can make re-identification easy.
If the dataset isn't large enough,
anonymization may fail,
and people can still be identified.
Second, we have Data Sanitization.
Data Sanitization ensures that data is completely removed
from the storage media
before it is disposed of, repurposed, or transferred.
This process is critical
for maintaining data confidentiality,
and preventing unauthorized access to sensitive information
that might otherwise be recoverable.
Imagine a company has decided
to upgrade its computer systems.
This upgrade includes replacing old hard drives
used to store sensitive customer information,
financial records, and other internal documents.
The company needs to dispose of these drives securely
to prevent any unauthorized access to the data.
Simply deleting the files, or formatting the drives,
might seem like enough, but it's not.
Deleted files are often just marked as free space,
and formatting only removes the file system references
without actually erasing the underlying data.
With the right recovery tools,
a hacker could still retrieve
valuable and confidential information from these drives.
To solve this problem,
a company implements Data Sanitization methods
to ensure that all data is permanently erased,
and cannot be retrieved.
One of the most common methods
they would use is overwriting.
In this process, the data on hard drives is overwritten
with random patterns, zeros or ones,
three, seven, or even more times.
This process replaces the original data
with meaningless information,
effectively destroying any traces of the files
that were originally stored.
Overwriting isn't the only way to sanitize data.
There are also tools available for secure Data Sanitization.
One popular tool is Microsoft's Sysinternals SDelete,
which securely destroys files
by overwriting them with random data.
For instance, when a company needs
to securely delete customer records
before recycling a computer,
SDelete can be used to make sure
that no sensitive information remains on the device.
Another method of sanitization is Degaussing.
Degaussing uses strong magnetic fields
to disrupt the data stored on magnetic media,
like hard drives or tapes,
effectively scrambling the information,
so it cannot be read again.
Degaussing only works on magnetic media,
so it will not work on a solid-state drive.
Also degaussing permanently destroys the media.
It can't be used again.
Physical destruction is another form of Data Sanitization.
This method involves shredding, crushing,
or incinerating storage devices
to make data recovery and reuse impossible.
For example, when banks need to dispose
of old servers containing sensitive financial data,
they often use industrial shredders
to break down the hard drives into little tiny pieces.
By physically destroying the drives,
they eliminate any chance of the data being reconstructed.
Data Sanitization is critical not only for businesses,
but also for individuals
who want to protect their personal information.
For example, before selling or donating an old smartphone,
it's important to conduct a factory reset,
and then run a Data Sanitization tool
to ensure that no personal photos, contacts, or messages
remain on the device.
This prevents anyone from accessing sensitive information
after the phone has changed hands.
So remember, data handling and management
should protect data throughout its entire lifecycle,
and ensure confidentiality, integrity, and availability.
Data handling and management concepts include
Data Anonymization and Data Sanitization.
Data Anonymization modifies data
to prevent the identification of individual data owners
while still allowing the data to be analyzed,
making it valuable without compromising privacy.
Data Sanitization, on the other hand,
involves securely destroying data
to ensure that sensitive information is completely removed
before storage media is disposed of,
repurposed, or transferred.
Together these processes help protect sensitive information,
maintain privacy, and ensure data security
in enterprise environments.