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With production data under lock and key, thanks to data privacy regulations limiting its use, and the complexity of that data steadily increasing as our environments rely on multiple data sources and a mashup of legacy and cutting-edge tech, the task of sourcing useful data for software development and testing is more challenging than ever. Data anonymization is an approach to making production data safe to use in lower environments by transforming real-world data into de-identified equivalents.
Under the umbrella of data anonymization, there are many methodologies and techniques that developers can use to anonymize production data. This guide will explore these approaches and offer best practices and tips for optimizing data anonymization for testing and development. The need for anonymized test data is particularly acute in regulated industries, like financial services, healthcare, insurance, and telecommunications; the use cases explored herein will be oriented around the needs of those verticals, whose data is riddled with sensitive information that must be protected to meet compliance requirements.
At a high level, data anonymization refers to the process of removing personal identifiers from a dataset so that individual identities within that data can no longer be identified. For all intents and purposes, the term data anonymization is synonymous with data de-identification, though various usages of “anonymization” can lead some readers to associate it with a more nuanced definition, either positively or negatively.
For example, the high-profile 2006 Netflix re-identification incident—in which an anonymized dataset of Netflix users was reverse-engineered to reveal the users’ identities by combining the anonymized data with other publicly available datasets—left many with a negative impression of anonymization as not being a secure or reliable approach. Conversely, GDPR outlines a very precise definition of anonymization that establishes it as a process that should fully exclude the possibility of re-identification, setting a high bar for the quality of anonymized data. Readers in the EU should adhere to that definition when using the term.
For the purposes of this guide, we will treat data anonymization as synonymous with data de-identification, having the goal of removing personal identifiers from a dataset to safeguard data privacy and, same as GDPR, making the data transformation irreversible. In the realm of software development, this is an essential technique when working with production data that contains sensitive information that should not be accessible to developers or used as test data in lower environments. At the same time, for the purposes of effective development and testing, data anonymization must be performed in a way that both safeguards data privacy and safeguards the utility of the anonymized data in development and testing workflows. In other words, simple redaction is not enough.
The market demand for data anonymization can be summarized as such: more data, more problems. The scale of data that organizations are collecting today continues to balloon, including sensitive data throughout. Data breaches are also on the rise, meaning that more companies and their customers are at risk of sensitive data leakages. And rightfully as a result, data privacy regulations and associated fines for compliance infringement continue to become more stringent and enforced throughout the world.
It is no surprise that the need for effective data anonymization is greater than ever before. The increasing emphasis on consumer privacy is driving more and more industries to find and implement data anonymization techniques in order to maintain consumer trust and regulatory compliance. The regulated industries of financial services, healthcare, insurance, and telecommunications have traditionally led the way, thanks to their heavy reliance on sensitive personal data. But industries like e-commerce, edtech, and logistics are also following suit, given the broader coverage of today’s privacy regulations.
The global market for data masking, a key data anonymization technique, is expected to witness substantial growth, driven by stringent regulatory demands and the increasing need for secure data environments. This is in spite of the rise of generative AI, as that wave of technology has yet to crack the highly complex mathematical nut that is generating a complete and referentially intact structured database for development and testing. Generative AI has not yet replaced the need for traditional data de-identification, (though this is a space of active research, including here at Tonic.ai).
Data anonymization is a very relevant and useful method for generating safe, realistic test data in regulated industries and beyond, to help organizations comply with GDPR, HIPAA, etc., without getting in the way of developer productivity. That said, not all anonymization is created alike, and the technique you should use depends on what you need your anonymized data to do.
Within the realm of data anonymization, a variety of techniques are available for developers to transform their data effectively, each suitable for different types of data and use cases.
Note that, per the definitions laid out by GDPR, “pseudonymization” is not synonymous with “anonymization”, as pseudonymized data is re-identifiable when combined with other datasets, whereas anonymized data cannot be re-identified when combined with other datasets. And we firmly agree. We include a definition of pseudonymization below to help explain how anonymization got a bad rep in some readers’ eyes.
Data masking is the approach that most people think of when they talk about anonymization. It involves a one-to-one transformation that protects sensitive data by replacing it with altered values. Those altered values can be made more or less similar to the original values, depending on the specific masking method used. When done well, masking can be highly effective in creating an artificial yet functionally similar dataset for development and testing purposes. It allows developers to build using realistic data scenarios without exposing real-world data.
Pseudonymization is a less rigorous approach to de-identification. While, like masking, it replaces personal identifiers with artificial values or pseudonyms, it is done in a way that will allow for re-identification when combined with other data. Ultimately, this is the “anonymization” approach taken by Netflix in 2006; they unwittingly pseudonymized their user data when they meant to anonymize it, and as a result, the term anonymization got a chink in its armor.
Pseudonymization has its uses, especially in instances when the underlying data needs to be re-identifiable under controlled conditions, but it should not be considered synonymous with anonymization.
Often the most appealing approach in the eyes of the end-user, data synthesis involves creating entirely fictitious datasets modeled after real-world data to preserve the underlying structure and statistics. Technically, it isn’t so much anonymization as it is a full regeneration, though given that it can be generated based on sensitive data, it is worth including as an anonymization approach.
Thanks to the entirely fictitious nature of its generated output, synthetic data is often perceived as being “more” private and secure. But in the realm of structured data, in particular, it has its limitations. Data synthesis is a generative AI technique, and while it is useful on a column-by-column basis or even across multiple columns within a table, its technology isn’t yet able to synthesize complete developer databases.
That’s speaking within the realm of structured data. In the realm of unstructured data, meanwhile, it’s a somewhat different story. Data synthesis can be broadly applied across unstructured data to replace sensitive information with synthetic equivalents. For more information, see our unstructured data solution Tonic Textual.
Data generalization is the process of abstracting data to a higher level, like changing precise dates of birth into age ranges, to reduce the risk of individual identification while retaining some value for analytics.
Data swapping rearranges data values among records to obscure the original data’s structure. It's particularly effective in statistical databases, with categorical or continuous data types, to prevent the identification of individuals while maintaining the overall database integrity.
The pros of data anonymization far outweigh the cons, which is a nice thing to hear, given that anonymization is more and more a required step in developer workflows when it comes to sourcing test data. Ultimately, it comes down to the approach you take and the degree of data utility you’re able to achieve.
If you can efficiently anonymize data for development and testing while maintaining the data’s utility, data anonymization offers nothing but pros. On the other hand, if you’re grinding through inefficient approaches or weak transformations, you will likely feel a world of test data pain.
With that in mind, here are the pros of doing anonymization well and the cons of doing anonymization poorly.
From financial transactions to patient health records, anonymization finds extensive applications in regulated industries in particular, especially with the rise of consumer applications in these sectors. Here are a few examples of how these industries rely on data anonymization.
Ensuring the confidentiality of personal financial information is paramount. Developers building software and applications in the financial space rely on data anonymization to ensure both compliance and data security in their lower environments.
In healthcare, data anonymization is used to safeguard protected health information (PHI), aka patient data processed by the apps we all rely on to schedule appointments, process claims, and view our medical history. It goes without saying that this data should not and need not be accessible to the developers building those apps, but developers do need realistic, de-identified health data on which to build. Using anonymized healthcare data in developer workflows is required for compliance with HIPAA in the United States.
With overlap in both the financial and healthcare spaces, insurance companies likewise rely on data anonymization to develop better products and services while ensuring that individual policyholders' details remain confidential.
Telecom companies handle vast amounts of personal data that must be anonymized for use in development and testing. This can also extend to unstructured data, like customer chat logs, which need to be anonymized before they can be used to train an LLM for improving AI chatbots.
Governments are another big user of data anonymization. Whether for data analysis or to build software, they anonymize citizen data to protect personal information and enable transparency and accountability without exposing sensitive data.
Given the complex nature of our data ecosystems today, implementing data anonymization can be a complex undertaking. Much of the complexity stems from the need to maintain the consistency of the de-identified data across multiple data sources, as well as being able to transform data in a targeted way depending on the data type. There’s also the scale of today’s data to keep in mind. Anonymizing a complete database that might be several PBs in size may not even make sense from a computational perspective.
Solutions to these challenges include implementing a system that works seamlessly with all of your data sources, from Postgres to Snowflake to flat files, while ensuring input-to-output consistency and referential integrity across those various databases. The system should ideally offer a comprehensive gamut of anonymization techniques to enable you to craft each data type to the degree of realism required for functional testing workflows. Lastly, database subsetting is a must. Why anonymize PBs of data when you can scale that data down to a more manageable dataset that is representative of the full and much more efficient to de-identify.
Diving deeper into our point above about the available anonymization techniques, the choice of which technique you implement should be guided by the specific needs of your testing workflows, the type of data involved, and the regulations your data is subject to. If you’re working in the healthcare space, you’ll want anonymization methods that enable you to achieve HIPAA Safe Harbor or that work well with HIPAA’s Expert Determination approach. Financial services need anonymization approaches that will ensure the fidelity of statistical data, including algorithmic data transformations.
Overall, developers need to consider the balance they want to achieve between data utility and privacy, the robustness of the anonymization techniques they choose to apply given their regulatory environment, and any potential risks associated with data re-identification.
Where to begin when anonymizing data for development and testing? There’s more to it than just finding and removing sensitive information. The following best practices offer a solid workflow to follow, wth key considerations along the way.
You can’t effectively anonymize your data unless you know what sensitive data exists in your dataset and where it lives. Of course, knowing the ins-and-outs of a PB-scale dataset is unfeasible. Automated tools for scanning through your data to detect and flag sensitive information are an essential part of the process. Ideally, these tools can also be customized to detect sensitive data that is unique to your dataset. By “know your data”, we really mean equip yourself with the right tools that can surface what you need to know about your data for you.
For the purposes of developer and testing workflows, randomly anonymized data is not useful data. Broken Primary and Foreign Key relationships can break your automated testing suites. Values that are dependent upon values in another column in your production data must maintain that dependency in your test data. Ensuring input-to-output consistency in your data anonymization techniques allows you to maintain relationships and referential integrity across your anonymized database.
Fully anonymizing your production database and bringing the whole thing down to your lower environments is neither necessary nor efficient. Subsetting your data in tandem with the anonymization process reduces the processing time and generates a more manageable dataset for use in developer environments.
Effective subsetting can be tailored to targeted use cases, so each team or developer can get just the slice of representative data they need, and nothing more. This also adds a further layer of protection by reducing the overall footprint of your data and minimizing the amount of data at risk of leaking.
Make anonymization scalable both in terms of the amount and variety of the data you can handle, and the ability to automate repeatable tasks to streamline your workflows over all. On the data front, this means implementing a system that will work with whatever data you throw at it from whatever data source your team is using today or will be using tomorrow. A process built solely for Postgres won’t work for Snowflake or MongoDB. Your data and where it lives is subject to change; your anonymization solution must be flexible to adapt to those changes.
And of course, make your processes repeatable with built-in automations. This can mean the ability to refresh your anonymized data on demand when your production data changes, or configuring anonymization techniques once and automatically applying those configurations to the appropriate data automatically going forward.
To ensure regulatory compliance, data anonymization should not be a subjective decision made differently by different members on your team. It should be informed by a thorough understanding of the regulations with which your data must comply. Defining these requirements at the organization level allows you to incorporate this knowledge within your approach to data anonymization. The ideal approach should include solutions for setting anonymization policies so that everyone on your team anonymizes your data in a standardized and approved way. This not only strengthens data protection and governance, it streamlines the anonymization process by eliminating the need for decision making along the way.
Historically, teams have relied on building custom in-house scripts or acquiring legacy data masking solutions in order to anonymize production data for testing and development. These approaches are no longer sufficient to meet the demands of today’s more complex and sprawling data. We launched Tonic.ai to meet the needs of developers today, and our flagship products, Tonic Structural and Tonic Textual, offer cutting-edge solutions for anonymizing structured, semi-structured, and unstructured data.
Tonic Structural meets the needs of developers working with structured or semi-structured, equipping them with data anonymization, subsetting, and synthesis solutions to de-identify production data for safe and effective use in their lower environments. Its features include sensitive data detection, consistent de-identification, cross-database subsetting, and comprehensive data connectors to all the leading structured and semi-structured data sources.
Tonic Textual offers unstructured data redaction and synthesis for developers working with free-text data in both the software and AI spaces. Protect sensitive data in PDFs, Word documents, .txt files, and more, while maintaining the realism and utility of that data for testing and ML model training.
Both solutions are designed with the developer in mind, to allow you to incorporate sophisticated data protection measures seamlessly into your workflows, ensuring that data anonymization is both effective and efficient. The end result isn’t just anonymized data—it’s faster release cycles, better products, happier developers, and happier end-users.
To learn more about reaping the benefits of data anonymization in your developer workflows, connect with our team today.
The goal of anonymization is to eliminate the possibility and risk of reverse-engineering. The data transformation techniques offered within Tonic.ai’s products are built with this goal in mind, to enable our users to satisfy the anonymization requirements of GDPR, HIPAA, CCPA, etc.
While it is a significant part of compliance, GDPR also requires measures like data minimization and secure processing practices.
Synthetic data is generated based on a model rather than transformed on a one-to-one, row-by-row basis. That model can be trained on an existing dataset and then used to generate any number of statistically similar rows of data. Anonymization, meanwhile, transforms data one-to-one, taking each row in a dataset and altering it to output the row in an anonymized form.
