What Is Data Anonymization?

Data anonymization is the process of transforming information by removing or encrypting personally identifiable information (PII), protected health information (PHI), sensitive commercial data, and other sensitive data from a data set, in order to protect data subjects’ privacy and confidentiality. This allows data to be retained and used, by breaking the link between an individual and the stored data.

As data sharing and data exchanges become more common, anonymization reduces the risk of a data leak, re-identification, or noncompliance incident. Already a core tactic for complying with the EU’s General Data Protection Regulation (GDPR) and the US’s Health Insurance Portability and Accountability Act (HIPAA), data anonymization will continue to grow in importance as data security and privacy laws and regulations proliferate.

What is the Purpose of Data Anonymization?

Not all data needs to be anonymized, but all organizations should still have the ability to anonymize their data if necessary. The volume and speed at which data is being generated, collected, and used means that at some point, virtually every company – regardless of industry, size, or geography – will be subject to rules or regulations regarding sensitive data.

Sensitive data refers to the aforementioned PII, like first and last names, email addresses, and credit card numbers, as well as protected health information (PHI), including medical records, lab results, and medical bills. But it goes well beyond personal data. Commercially sensitive data encompasses business information like revenue, HR analytics, and IP, as well as classified information, like top secret, secret, and confidential data. In some cases, indirectly identifying attributes like hair color, height, and job title, can also be considered sensitive.

Given the breadth of this definition, it’s safe to assume that most organizations store and use sensitive data in some capacity – which is why data anonymization should be a core capability in any data stack.

According to DBTA’s report on the growing challenges of data security and governance, instances of data being compromised increased nearly 70% from 2020 to 2021, with the average cost of each data breach now totaling $4.24 million. Fines for GDPR-related breaches alone jumped seven-fold in 2021, totaling well over a billion dollars. And that doesn’t begin to cover the reputational damage and loss of trust that organizations experience for improperly securing sensitive data. Anonymizing data thoroughly removes sensitive information from data sets, vastly reducing the risk of these costly data leaks and breaches.

[Read More] How to Implement Snowflake Column-Level Security for PII and PHI

Data Anonymization Techniques

There are six common techniques used for data anonymization:

1. Data Generalization

Data generalization creates a broad categorization of data in a database, essentially “zooming out” to give a more generalized view of the data’s contents. Specifically, generalization occurs when protection measures map many different values to a single value. An example of generalizing data is grouping specific ages into age ranges or related job categories under a suitable umbrella term. Numeric rounding is another example of generalization. Typically, this technique is most useful when the generalization process introduces enough ambiguity to achieve privacy objectives, while ensuring the data remains sufficiently useful for its purpose.

2. Data Masking

Data masking is a method of data access control that hides values in a data set in a way that still allows access to the data, but prevents the original values from being re-engineered. Common data masking techniques include k-anonymization, encryption, and differential privacy.

3. Data Pseudonymization

Data pseudonymization is generally understood as the process of masking directly identifiable information by replacing it with an artificial identifier, referred to as a “pseudonym.” An example of this de-identification method is replacing a name with a number associated with that individual (i.e. Holly → 12, Todd → 33).

4. Synthetic Data

Synthetic data is machine-generated data that closely resembles sensitive data that should be kept confidential. It is often used for testing environments and to validate or train models for mathematics or machine learning (ML), by reducing risk to privacy.

5. Data Swapping or Data Shuffling

Data swapping, or data shuffling, repositions data in a data set so that attribute values do not match the original data. Also referred to as data permutation, an example of this technique is swapping one patient’s age and medical diagnosis with another’s, but not changing their names.

6. Data Perturbation

Data perturbation makes data more ambiguous by randomizing data elements — for example, by adding random noise to sensitive numerical attributes, or randomly introducing changes to categorical variables such as diagnostic codes. Though seemingly destructive, when performed in a deliberate and controlled manner, these techniques serve to make individual records less sensitive while having predictable — and correctable — effects on aggregate analysis. An example of successful data perturbation is when a survey that calls for individuals to report recreational drug use no longer reliably implicates survey takers who may point to randomization as the source of their “yes” response, yet the expected number of artificial “yes” answers can often be precisely estimated and corrected, even in relatively small-sized subgroups.

When Do I Need Data Anonymization? Top Use Cases

The need for data anonymization spans across all industries and geographies, so there is no shortage of examples for when or how it may be used. That said, here are a few use cases that rise to the top:

Data Sharing

Since data anonymization fully removes or transforms sensitive information in a data set, there is much less chance of relevant information being used to re-identify an individual or entity. This is key for secure data sharing because regardless of where or how data is shared – whether across departments, industries, or borders – removing or transforming sensitive data makes it possible to greatly decrease the risk that confidential information can be leaked or re-identified.

The GDPR, arguably the toughest modern data use regulation, applies to any person or organization that processes the data of individuals located in the EU, and imposes restrictions on international data transfers. While the regulation incentivizes pseudonymization and anonymization, it does not consider anonymous data to be personal data. Therefore, data anonymization can drastically alleviate regulatory burden and achieve use cases, including cross-jurisdictional data sharing. This extends to the GDPR’s storage limitation requirement as well, allowing organizations to store anonymized data over longer time periods and improving their ability to identify enduring trends and create predictive models.

Customer Experience & Product Innovation

The way in which customers shop and use products is invaluable information for B2C and B2B companies alike. Customer data can help improve everything from app layouts and shelf placements to product recommendations and new capabilities. However, customer data can also be sensitive – and if you’ve ever talked about a product and then received an online ad for it, you know how off-putting it can feel.

To strike a balance between preserving customers’ privacy and using their data for legitimate business purposes, such as to improve their brand or product experience, organizations can use data anonymization. Removing or transforming personal identifiers from customer data helps ensure that data can be shared and analyzed without having to collect informed consent. The anonymization process, however, remains within the scope of data protection regulatory frameworks, meaning you must comply with data protection principles when initiating an anonymization process.

Demographic Information & Reporting

For public sector agencies and organizations, publicizing demographic information is not just important – it’s expected. For instance, when choosing a new place to live, many people consider factors like cost of living and crime rate. This type of data is often derived from individuals’ data and then aggregated, but it must be safely anonymized so as not to expose personal records.

In 2020, the US Census Bureau announced for the first time that it would use differential privacy, a type of dynamic data masking, to protect demographic data. This movement toward large-scale adoption of data anonymization indicates that risk mitigation requirements are impacting a wide range of stakeholders, even those working with aggregates only.

Medical Research

Like the GDPR, HIPAA’s Privacy Rule mandates that PHI must be de-identified in order for it to be used, absent written patient authorization. Anonymizing sensitive patient data opens the door for medical information to be analyzed and shared in a way that protects confidentiality. This is critical for research on diseases, treatments, community health, and more.

Data Anonymization in Practice

How are modern teams applying data anonymization to protect their sensitive data?

One of the world’s top streaming services needed to ensure that they could collect subscriber data to provide a refined customer experience without risking noncompliance or inadequate privacy. By applying automated privacy-enhancing technologies (PETs) across their Snowflake and Databricks platforms, this organization’s DataOps team was able to consistently anonymize subscriber data containing PII throughout their data ecosystem. This enabled the team to continue analyzing subscriber data while maintaining customer privacy standards and compliance with expanding regulations.

In the financial services realm, one top global bank was required to prohibit their trading teams from seeing pending client orders in order to remain in compliance with rigorous banking standards. However, the electronic trading desks still needed access to client transaction data in order to ensure the timely completion of said transactions. To balance these needs, the bank applied automated anonymization techniques to ensure that all client transaction data was anonymized, therefore blocking traders from knowing which data applied to which clients and avoiding conflicts of interest.

Lastly, pediatric behavioral health company Cognoa had to find a way to provide their data scientists with “same day same hour” access to fresh patient data without exposing sensitive PHI to any undue or risky access. What’s more, this access had to be both FDA- and HIPAA-compliant. Using dynamically-enforced access policies, the Cognoa team is able to anonymize sensitive PHI based on user access permissions, providing teams with up-to-date information while maintaining regulatory compliance and ensuring patient privacy.

Implementing Data Anonymization at Scale

Anonymizing data is highly valuable but by no means trivial. In today’s fast-paced world of data analytics, organizations need a solution that will allow them to apply data anonymization techniques at scale, without creating bottlenecks or putting data security at risk.

The Immuta Data Security Platform does just that, delivering secure data access at scale. Immuta’s dynamic data masking capabilities include advanced privacy enhancing technologies (PETs) like randomized response and k-anonymization, helping to secure even the most sensitive data without sacrificing utility. By automating data discovery, security, and monitoring, Immuta ensures that users have access to the right data at the right time – so long as they have the rights.

See how easy it is to anonymize data by creating and enforcing plain language policies across platforms by requesting a demo from one of our experts.