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DATA ANONYMIZATION TECHNIQUES: PROS AND CONS


Tova Dvorin|
February 15, 2023


Learn more about secure, collaborative computing Request a demo


Back


DATA ANONYMIZATION TECHNIQUES: PROS AND CONS

Tova Dvorin|
February 15, 2023


Learn more about secure, collaborative computing Request a demo


Post

Data analysis is a central part of business operations today as it helps
organizations save cost and generate new revenue, in many cases by gaining
insight into customer preferences and maximizing returns by customizing their
offerings. However, some of the data that is held by businesses is sensitive
with potential to  compromise user privacy and security. As a result, several
regulations such as the General Data Protection Regulation (GDPR), the Health
Insurance Portability and Accountability Act of 1996 (HIPAA), and the California
Consumer Privacy Act (CCPA) have been developed.

Data anonymization is a method commonly employed by businesses to enable the use
of the information they have without comprising user privacy and security.  In
this blog, we will examine data anonymization as an approach, its drawbacks, and
its advantages.  

What is Data Anonymization?

Data anonymization is the process of removing or hashing various data points
that link a particular piece of data to an individual. This process lets
organizations store and exchange customer data that can be used for purposes
such as analytics, visualization, or sharing with third parties without
revealing any connection of the data to a particular person.

Data anonymization usually retains as much data as possible, and the anonymized
data tends to resemble the original dataset yet with less granularity. For
example, if your organization gathers full DOB (mm/dd/yyyy), it can be
anonymized by hiding the month and day and retaining only the year, thereby not
exposing the personally identifiable information (PII). 


DATA ANONYMIZATION TECHNIQUES

Here are some of the most common data anonymization techniques employed today.


DATA MASKING

Data masking involves creating a fake,  but structurally similar version of your
data. This is accomplished through modification techniques such as shuffling,
simple word or character substitution, encryption, or masking out certain data.
For example, the letter “R” can be masked as “L” through substitution masking,
or credit card numbers masked out as “**** **** **** 7598.”


PSEUDONYMIZATION

Pseudonymization is the process of removing identifiers from a data set and
replacing them with a pseudonym. The main aim of this anonymization technique is
to ensure that particular data can’t be matched to an identifiable person unless
it is combined with a separate set of information.

A simple method of pseudonymizing data is substituting a person’s name with a
fake name (a pseudonym.) For example, if a user submits the name “Jane” during
registration, your main database can simply store it as “Person 2647.” The
algorithm mapping Person 2647 to Jane can then be stored in another secure
database.


GENERALIZATION

Generalization is the process of removing more specific aspects of data to
reduce its identifiability. This is essentially like zooming out, where you hide
the finer details but still maintain a high level of accuracy that can be used
for analysis.  For example, if you have a data set that states the age of each
person, it could be generalized using categories such as 21 to 25 and 26 to 30.
You can also generalize an address by removing the house and block number while
retaining the street name, city, or zip code.


DATA SWAPPING

Data swapping is a simple method of anonymization that involves switching
attributes in a certain column of data with others in the same column. This
means that you will end up with a shuffled database that does not disclose any
specific information about any natural person at the end of the process. 

Assume that you have the database below.

First Name Last Name D.O.B City John  Maxwell 12/4/1985 London Claire Cook
3/7/1994 New York Matt Jansen 5/10/1991 Amsterdam Susan Clark 17/11/1989
Stockholm

The data can be swapped as shown below to create anonymity.

First Name Last Name D.O.B City Matt  Clark  5/10/1991 London Claire Maxwell
12/4/1985 Amsterdam  Susan  Cook  17/11/1989 Stockholm John Jansen 3/7/1994 New
York 



THE PROS & CONS OF DATA ANONYMIZATION

There are many advantages derived from data anonymization which mostly center on
privacy.


ADVANTAGES OF DATA ANONYMIZATION

PREVENTS DATA MISUSE

According to the 2021 Verizon Data Breach Investigations Report, insiders are
responsible for around 22% of security incidents. Data anonymization helps 
prevent unintentional misuse or exposure by users authorized to access sensitive
data.

EASY TO IMPLEMENT

Anonymization mostly uses simple algorithms to swap, generalize, pseudonymize,
or mask particular data. This makes the process cost effective, fast, and easy.

ACTS AS A DAMAGE CONTROL MEASURE

No system is 100% foolproof, so you always need to prepare for possible
infiltration. But in such a case, data anonymization can help protect sensitive
data from compromise as the data wouldn’t make much sense to the attacker. The
process also helps curtail data loss damages in a database breach.

REGULATORY COMPLIANCE

The European Union’s GDPR requires that data of individuals living in the EU
undergo pseudonymization/anonymization. From there, the data is no longer
classified as personal data, and it can be used for broader purposes without
breaching compliance regulations.

ENHANCES BUSINESS PERFORMANCE

Since anonymized data can be analyzed and used without breaching compliance
standards, businesses can use the data to get insights into their customers and
offer better and improved services.

PROTECTS BUSINESS AND BRAND REPUTATION

Data anonymization is part of the larger duty of an organization to protect
sensitive, personal, and confidential data The loss or breach of this
information can lead to a possible loss of trust and market share.

 


DISADVANTAGES OF DATA ANONYMIZATION

LESS ACCURATE ANALYSIS

Reducing the granularity of data stored and analyzed, results in less meaningful
information and less accurate insights. 

DOESN’T MAINTAIN DATA RELATIONSHIPS

Data anonymization reduces the granularity and the accuracy of the data,  hence
in some cases scrambles the relationships between data points. The relationships
that are lost are critical for any artificial intelligence or data science
activity. Therefore, anonymized data is limited in the utility that can be
derived from it.

ONLY USEFUL FOR AGGREGATE DATA

Data anonymization is only useful if you need to summarize aggregate data, the
goal of these types of methods is to perform statistics on the data sets. The
technique cannot be used to analyze individual record-level data, in which the
personally identifiable data is highly relevant to the analysis. In other cases,
like in health research, this means that if analysis reveals that a specific
subject is at high risk for a fatal disease, there is no way to identify that
individual to alert them of findings and get this critical information to the
individual whose health is at risk. Data anonymization also renders the data
useless for the personalization of targeted offers, as the ability to connect
insights with an individual has been destroyed.  

PRIVACY RISK REMAINS

Most forms of data anonymization can be reverse engineered by acquiring an
external data set. For example, in the case of pseudonymization, if an insider
already has access to pseudonymized data, they would only need to gain access to
the pseudonym database to de-anonymize the entire data set. In a recent case, a
newspaper purchased anonymized Grindr user data from a 3rd party broker and
device ID and location data to re-identify the account as having belonged to a
local priest. The newspaper published the information and the priest
subsequently resigned.  Although the risk is reduced, it is far too easy to
re-identify anonymized data.

Data cannot be linked across multiple data sources

In cases where one would like to link data on a record level across multiple
databases, for example, combining patient data from a genomic database, a
clinical database, and a wearable database. Or in a fintech setting linking data
on individuals for banks, telcos, and insurance cannot be done, and the key on
which the records are linked is exactly the identifiers these techniques
eliminate.

Control over data usage in collaboration settings

Anonymization techniques do not allow the data owners to have any control over
what is done with the data once anonymized and transferred to a third party.
Once the 3rd party receives the anonymized data it can use it in many ways,
including to re-identify the data, like what happened in the famous Netflix data
de-anonymization scandal.

Data Anonymization Techniques – Pros and Cons

The main benefits of data anonymization are that it is an easy, inexpensive way
to protect privacy when performing analysis on aggregated or individual data.
However, in most cases, the shortcomings far outweigh the benefits. Data
anonymization produces less accurate results and does not allow for data
linkage. It’s also not very secure and re-identification is easily achieved.
Neither does it allow any control over how the data and models are used, or
protection of the data and model IP. Yet, perhaps the most challenging aspect of
data anonymization comes when one wants to collaborate with 3rd parties. Data
cannot be linked across multiple databases when it’s anonymized. The same goes
for cases when one aggregates anonymized data, you cannot remove deduplications
and create biased data sets.

Data anonymization techniques are mentioned explicitly as a required or accepted
technique by many data privacy regulations, but that doesn’t mean they are
secure, it really depends on the type of analysis and utility one is aiming to
gain. The selection of privacy enhancing tools and technologies needs to be
considered on a case-by-case basis, but data anonymization should be used very
cautiously as it has been proven to be fairly easy to breach. Data-driven
enterprises looking for ways to derive more value from data require a holistic
privacy preserving data collaboration platform that allows flexible selection
and combination of multiple privacy enhancing technologies (PETs) as needed
across the organization and data sources. 

See how enterprises are using Duality in our Demo Library.

 

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