5 Data Anonymization Techniques You Should Know

The world revolves around data. This trend has led to many positives — like personalized shopping experiences and exciting AI applications — but it also raises questions about privacy and security. Data anonymization techniques can help businesses capitalize on the good side while accounting for these concerns. 

What Is Data Anonymization?

As you may guess from the name, data anonymization is the process of making data anonymous. More specifically, it removes, changes or otherwise hides details so you can’t tie the data to specific individuals.

Anonymization is important because it protects users’ privacy. If you make your customer records anonymous, a data breach won’t be as impactful. The attackers won’t be able to gain any personally identifiable information (PII) from it, so they can’t do much damage with it. It’s worth noting, though, that some data anonymization techniques are reversible, so it’s not a 100% guarantee of security.

Some laws require or heavily recommend anonymization. The General Data Protection Regulation (GDPR) says its principles don’t apply to anonymous data, so anonymization lets companies store and use information to much further extent.

Common Data Anonymization Techniques

Whether you’re running a company’s marketing division or building a machine learning model, anonymization can be a crucial security step. You can go about it in many different ways, too. Here are some of the most popular data anonymization techniques.

Generalization

Generalization is one of the simplest forms of data anonymization. This technique hides sensitive details by removing specific information. It swaps detailed records for generalizations, such as switching someone’s address for just their street name.

Data generalization is easy to implement and removes PII, but it comes at the cost of making data less useful. If you’re trying to personalize recommendations to a specific user, this method may get in the way. The key lies in balancing between keeping information detailed enough to be accurate and vague enough to be useless to criminals.

There are two main approaches to generalization — automated and manual. Automated generalization tools can perform this anonymization quickly, but humans are often better at striking the ideal balance.

Data Swapping

Swapping is another relatively simple data anonymization technique. Also known as permutation or data shuffling, this approach masks data by switching information from one record with another. For example, you could switch two users’ birthdays or shuffle the addresses in a data set.

Data swapping keeps all the same information in the data set, but where each record belongs will be different. That’s not ideal for security purposes, but it is helpful when training machine learning models. By swapping attributes, you can prevent bias — one of AI’s biggest ethical concerns — because prejudice-prone data points won’t match their original records.

Some details have a bigger impact when swapped than others. What those attributes are varies depending on your use case, so it’s important to determine which changes could yield which results.

Data Masking

A more privacy-minded way to anonymize data is to mask it. Data masking starts with creating a copy of your original data set. It then alters the data by substituting words, characters or other values for random placeholders. It’s similar to how encryption works, but there’s no decryption on the other end. You simply keep using the altered data set.

In masking, all the values and their relationships within a data set remain unchanged, but the information itself doesn’t contain any real-world details. That way, it’s still accurate enough for AI training or other analytics processes, but it’s useless to cybercriminals.

The biggest security advantage of data masking is that it’s irreversible. Not knowing what real-world people or things a data set represents can make some use cases hard, but it’s great for privacy.

Pseudonymization

Pseudonymization is similar. This technique also swaps data for placeholders but focuses on changing PII to something that serves the same purpose but doesn’t reflect the real world. The most straightforward example is changing users’ names to fake ones like John Doe.

Like in data masking, the relationships between data points in a set remain the same after pseudonymization. You can change someone’s name and email address, but the changed address still belongs to the same person. That way, you can still use the data for highly accurate analytics without risking any PII exposure.

It’s important to recognize that pseudonymization only changes direct identifiers. Clever cybercriminals may still be able to figure out who’s who from breached records, but it’ll take more work. For that reason, regulations like the GDPR may not accept pseudonymization as an acceptable form of anonymization.

Synthetic Data

Creating synthetic data is one of the most advanced data anonymization techniques. Synthetic data takes masking and pseudonymization further by creating entirely new data sets. Instead of changing some values, it generates new data that behaves like real-world information but has no relation whatsoever to real people.

Synthetic data comes from AI models that learn how real data behaves to accurately create fake but correctly functioning data sets. Despite not reflecting any real-world information, this data is just as effective as real data sets when training predictive models.

Synthetic data isn’t applicable in every use case. It’s not very helpful when personalizing services, for example. But where it is practical, it’s the most secure option. 

Choosing a Data Anonymization Technique

With so many data anonymization techniques to choose from, it can be hard to know which is best. Which method is best depends on your needs and what you’ll use the data for.

If you’re building an AI model, you likely don’t need anything reflecting real-world people. In that case, synthetic data is best if you have the right budget and computing power. Data masking or pseudonymization are more accessible alternatives as long as they meet your industry’s security demands.

If you want to analyze data to learn more about real people, you’d prefer a method like generalization or swapping. The former is best for user-specific applications that don’t need highly precise details. The latter may be better if you need specifics on some details but not others.

Use These Techniques to Protect Your Data

Regardless of which technique you use, data anonymization is an important step in protecting people’s privacy. These methods let you take full advantage of your data’s potential without accidentally revealing sensitive information in the process.