Data anonymization is essential for realizing the value of Personally Identifiable Information (PII). In today’s digital-first era, this value takes many forms. As a competitive differentiator, where data delivers deep strategic insight. Or as highly prized intellectual property, where data informs new product roadmaps. Or even as the foundation for global organizations, with business models that rely on constant customer and user data.
Traditionally, data’s value came from the insights within its vast volumes.
However, nowadays, extracting the most value from your data isn’t just about gathering as much of it as possible. It’s just as much about which channels it comes from, how fast you can collect it, and the level of security and compliance involved in your collection of data.
This new reality requires insights to be shared among relevant people and roles – without compromising PII.
Where the data allows business leaders to make informed decisions based on secure, compliant, and real-time data. And where the data also allows consumers to receive on-demand experiences and personalized interactions.
“To thrive in the Age of the Customer, companies cannot rely on retrospective analytics. True customer obsession requires harnessing insights in real time”.Forrester
For those tasked with managing data access governance and data access control, it’s about making things a win-win situation for all.
While also maintaining compliance, visibility, and transparency. End to end, and adapting to the many policies and rules that need applying and tracking around PII.
The solution, especially for those in highly regulated industries, is data anonymization.
What is data anonymization?
Data anonymization involves a mixture of removing identifying PII and/or encrypting sensitive information contained within data.
This process allows you to retain information and insights. At the same time, you also protect, secure, and anonymize personal data.
Successful anonymization means consent or authorization from subjects is no longer required. This makes it a practical way for organizations to ensure data access control, while realizing value from data processing.
How anonymized data relates to global data protection trends
It’s clear the future will contain more data access governance, data access control, and data anonymization. We’re already seeing how some of the highest-populated countries are taking action.
For example, China introduced two new laws in the fall of 2021 – the Data Security Law and the Personal Information Protection Law.
Organizations contravening the law may be effectively banned from processing Chinese personal data, with large fines (up to 5% of annual revenue) a possibility.
This is an unchangeable clause, meaning any future legislation will only further the expansion and protection of data privacy.
This summer saw the first steps toward federal data privacy legislation in the United States, via the America Data Privacy and Protection Act.
With so much data and so many data sources, these government-led initiatives are why it’s never been more important for organizations to gain full control and visibility throughout the data lifecycle.
After all, within the EU there are 20 million reasons to ensure correct data anonymization for PII. That’s the maximum fine (or 4% of annual turnover) in euros, under the terms of the region’s General Data Protection Regulation (GDPR).
Naturally, there are multiple ways to manage these risks and anonymize PII.
What are some common data anonymization techniques for PII?
Below are some common methods for anonymizing PII. These have been developed to minimize potential risks and penalties arising from data-related cyber threats, attacks, and breaches.
Some data anonymization techniques are irreversible; others are reversible and can allow reidentification through decryption. For example, in the event of a subpoena or mandatory key disclosure.
Real values such as words or characters are swapped for fake information.
This anonymization technique is useful when you want to test how a database could be crunched or analyzed, without having to use real data.
Even if an attack is successful, masking the data renders it useless to any malicious actors.
You may want to keep data accurate enough for analysis, while reducing its granularity.
For example, for a local neighborhood, one option is to remove specific apartment details and simply keep the data at the street name level.
This form of database anonymization is also known as data shuffling.
It’s where the original data remains available, with values moved around or shuffled. For example, swapping around a data set’s ages and interests.
This method results in anonymized data that’s been modified only to a certain degree. Enough to fulfill database anonymization requirements, yet not enough to reduce the insights contained within the data set.
For example, rounding numbers or distorting the values with multiplicative or additive noise.
You may have seen confidential or classified items released with values removed or obscured.
This is often used so that sensitive documents exist and can be shared, without compromising certain aspects of the data.
Any sensitive data is deleted from the data set.
A series of NULL values or attributes is shown instead.
As the name indicates, this method involves pseudonyms. These are used in place of any identifying values. This is a GDPR-recommended form of data access control.
However, the original information is not deleted, and can be reversed. For this reason, data is classified as pseudonymized rather than anonymized.
This technique turns data into code. The code is encrypted to everyone apart from approved users, who require a key to decrypt the data set.
The decryption makes this another technique for pseudonymized data, which may be required by regulators as an alternative to data anonymization.
Hashing involves turning a given key, string of characters, or other PII into another value. Functions or algorithms map these values, so they stay discoverable without revealing the original source.
What’s more, the process is unidirectional, so hackers can’t reverse the hashed item back to its original source.
Take a distinguishing value, such as a person’s name. Then turn the value into a generalized name. Group these values into smaller buckets. Separating data in this way is a form of bucketing.
The personally identifiable element can be removed, while the data can still be used for analysis.
This method sees sensitive data replaced, or tokenized, with non-sensitive values. For example, a personally identifiable bank account number is turned into a random string of characters.
The sensitive data is still stored in a centralized location. However, during transactions the tokenized data isn’t linked – or exposed – to the original PII.
Training a machine learning algorithm requires huge volumes of data. Of course, this can be a problem when that data contains PII. Enter synthetic data.
Data is generated and synthesized based on the original source. The resulting insights can then be used – without being routed back to original subject sources.
Types of data to be anonymized – top five industry use cases
The use case is a crucial factor when deciding whether you should anonymize data. Along with a robust data anonymization policy to define the values to be anonymized.
For example, imagine you have a physical retail store and want to know the sections that attract the most footfall.
It may not be necessary to hold this type of data on specific customers’ movements. Your visitors may not welcome such a level of potential personalization, such as through push notification ads that address them by name.
In this use case, you can anonymize personal identifiers.
Your organization can then hold this data without requiring consent from each individual. Any push notifications can still be used, without having to put in place specific policies around targeted ads.
For these sensitive data sets, the objective should be anonymizing any data that could identify subjects.
This could include name, address, ID, and anything else that could be used by either the data controller or a third party.
Here’s how such considerations play out in the following industry use cases:
Highly targeted attacks in this lucrative industry are one reason why the likes of PCI DSS have been developed. These standards are for any institution accepting credit card payments.
Data anonymization helps institutions meet the 12 PCI DSS requirements, while also still being able to offer customized products to specific audience segments based on their income and financial health.
Anonymizing PII enables telcos to understand urban mobility, based on cell phone connections to cell towers. This was crucial during the pandemic, when there was a need to monitor and predict general population movements.
As an example of balancing insights with anonymization and privacy controls, a Vodafone study during this time only focused on groups of 50 people and above, rather than individuals.
Within healthcare, successful data anonymization can make it possible to conduct vital research projects without compromising patient privacy – this is vital, considering the often sensitive nature of medical data. Medical organizations can track sensitive data regarding movements of illnesses and performance of clinical trials.
Insurance companies also need to be able to analyze data, to develop and offer health plans for multiple target audiences.
The rise of EdTech and other online learning applications offers many data-driven possibilities around understanding academic performance. For example, gathering historical results to predict future performance among different groups.
Anonymization is required to safeguard children’s data and avoid personal identification, with US platforms required to comply with the Children’s Online Privacy Protection Act.
Utilities (oil and gas)
Smart meters, electric cars, demand for renewable energy – the transformation of oil and gas is well underway. Across the utilities industry, data volumes are growing exponentially, as more homes and businesses make use of Internet of Things devices.
Anonymizing this data will be essential to continue gathering usage-based insights without compromising customer privacy.
Anonymizing data with Velotix: How to get started
Data anonymization is a win-win. Your organization can be freed to extract insight from your data, while also being able to fulfill legal obligations around handling and processing.
You just need the right solution to help you identify the personal identifiers to be anonymized. So that your business can start maximizing value and minimizing risk. And that’s where Velotix comes in.
The AI-powered engine learns and identifies what needs anonymizing, in addition to giving you multiple data anonymization methods and obfuscation tools. These are built for a variety of use cases, such as masking and partial masking, row-level filtering, hashing, and bucketing. Giving you end-to-end transparency with automatic data privacy control.
What’s more, you can get started with existing data. Velotix auto-tags your catalogs, identifying any sensitive data and PII within your volumes. It’s a way to instantly gain industry-standard compliance and restrictions for GDPR, HIPAA, CCPA, and other regulations.
Contact us to learn more or book a Velotix demo. Discover how you can mitigate risks, increase visibility, and transform your data anonymization processes.