Simon Coulthard July 16, 2024
Data anonymization and pseudonymization are two methods that businesses can use to protect the personal data and identities of their customers, and they're key components of privacy-compliant software.
But understanding which one to adopt can be tricky. Each one is suited to particular data processing use cases and meets different legal data privacy thresholds, and there are also other practical distinctions to consider.
If you’re trying to work out which approach is right for your company - or which martech to adopt - then this blog is a great place to start. In it, you’ll learn about the differences between anonymization and pseudonymization, and the techniques used by each.
Then, you’ll see how they’re addressed in key global personal data protection laws and read about synthetic data anonymization - an innovative approach that is growing in popularity.
Let’s dive in!
Keep pace with the fast-moving world of privacy-first analytics. Subscribe to our newsletter and get monthly TWIPLA updates alongside digital optimization insights, direct to your inbox.
First off, it’s important to understand how anonymization contributes to data privacy and data security - two overlapping focus areas that feed into the wider field of data protection.
For businesses that operate online, safeguarding personal information is more important than ever. Strategies and processes need to be put in place that protect data from unauthorized access, misuse, disclosure, alternation, and destruction - as well as the obvious threat from hackers and other online criminals.
UThis work will ensure that businesses can fully tap into the benefits available from customer data - as well as that of employees, partners, and other stakeholders - while maintaining the individual privacy of these data subjects.
Personal data protection is also a fundamental legal requirement. Regulations such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States, and other national and international laws provide frameworks for protecting personal data that businesses need to comply with.
Today, there are over a hundred different privacy laws in place globally and they make businesses responsible for implementing robust data protection measures, secure data storage, and to ultimately ensure that personal information is handled responsibly.
But personal data protection can also give businesses a strategic advantage.
It can help build trust at a time when people are all too aware of the dangers that their data opens themselves up to online, enhancing a company’s reputation. It also reduces the risk of data breaches, and the financial and reputational fallout. Moreover, protecting personal data will allow businesses to avoid heavy fines and legal penalties if they’re found to have fallen short of legal expectations.
Two key techniques often employed to enhance protection are personal data anonymization and pseudonymization. These methods play a crucial role in reducing the risks associated with data breaches and unauthorized access while maintaining the utility of the data for analysis and decision-making.
Understanding the differences between these techniques and their appropriate applications is essential for organizations looking to comply with data protection regulations and safeguard individual privacy. By effectively utilizing anonymization and pseudonymization, organizations can protect sensitive information, minimize privacy risks, and thereby ensure legal compliance and guide business success.
Data anonymization refers to the process of erasing or encrypting private or sensitive information so that the data subject can’t be identified at any point in the future.
And once this personally identifiable information (PII) has been scrubbed by data anonymization tools, the data can then be used by businesses to analyze trends and patterns without having to worry about putting the data subject at risk.
Crucially, anonymized data is also unregulated. This means that businesses can sell it to anyone, or use it for whatever purpose they want.
However, the data anonymization process does also come at a cost. Anonymizing data and deleting personally identifiable data points will obviously limit a business’s ability to drive value and insight from it. So for instance, scrubbed data isn’t very useful for personalizing the user experience that is so vital for a business’ digital marketing.
In practice, data anonymization is done be creating a mirror image of a database, and implementing one of the following data anonymization techniques:
Pseudonymisation (or pseudo anonymized data) is the process of replacing any personally identifiable information (PII) with pseudonyms or aliases in a way that allows for this anonymization to be reversed with the right access.
Like data anonymization, this method can reduce the privacy risks for data subjects, and can help businesses to meet some (lower) data protection standards - including privacy by design and data security. However, pseudonymized data isn’t as risk-free as anonymization, and the information is still considered personal data.
In practice, pseudonymisation is done by employing any of the following techniques that replace, remove or transform any information that can be used to identify individuals, and which can be undone at a later date with the right access:
Below, you’ll find a table that compares data anonymization and pseudonymization side by side:
Anonymization | Pseudonymization | |
Definition | Removing or altering identifiable information to prevent any re-identification. | Replacing identifiable information with pseudonyms or aliases, allowing re-identification. |
Reversibility | Irreversible, cannot revert to the original data. | Reversible with the use of a secure key or reference table. |
Data Utility | Data utility may be reduced due to significant alterations or removal of data points. | Retains high data utility as data structure and relationships are maintained. |
Controlled Access | No re-identification possible, ensuring complete de-identification. | Only authorized personnel can re-identify data under strict security measures. |
Use Cases | Public data sets, statistical analysis where individual identification is not required. | Medical research, financial transactions where re-identification may be necessary. |
Security Requirements | No ongoing key management required, but significant effort needed to ensure data is sufficiently anonymized. | Requires robust security measures to protect pseudonymization keys. |
Regulatory Compliance | Complies with regulations, ideal when the risk of re-identification must be minimized to zero. | Complies with regulations like GDPR, allowing data processing while safeguarding identities. |
Complexity and Cost | Effort required to ensure effective anonymization but eliminates ongoing key management. | Can be complex and costly due to secure management of keys and re-identification controls. |
Different legal data privacy requirements exist for data anonymization and pseudonymization because anonymization irreversibly removes identifiable information, offering higher protection, while pseudonymization retains reversible pseudonyms, providing lower protection and allowing controlled re-identification.
Below, you’ll find information on what the main global data privacy laws have to say about these data anonymization and pseudonymization methods:
Law/Regulation | Anonymization Requirements | Pseudonymization Requirements |
GDPR (General Data Protection Regulation) - EU | Encouraged for minimizing data protection risks. | Recognized as a security measure to reduce risks. |
If data is fully anonymized, it is no longer subject to GDPR. | Allows for lawful processing of data while preserving the ability to re-identify. | |
CCPA (California Consumer Privacy Act) - USA | Anonymized data is not covered under CCPA. | Considered a method to protect personal data. |
Must ensure data cannot be re-identified. | Pseudonymized data may still be subject to CCPA if it can be linked back to an individual. | |
HIPAA (Health Insurance Portability and Accountability Act) - USA | Requires de-identification of data to meet either Safe Harbor or Expert Determination standards. | Permits the use of pseudonymization for research and healthcare operations. |
True anonymization is not explicitly required but achieving de-identification is essential. | Re-identification keys must be securely maintained. | |
PIPEDA (Personal Information Protection and Electronic Documents Act) - Canada | Anonymized data is excluded from PIPEDA’s scope. | Pseudonymization is recognized but treated as personal information since re-identification is possible. |
Must ensure data is irreversibly anonymized. | ||
LGPD (Lei Geral de Proteção de Dados) - Brazil | Anonymized data is not subject to LGPD. | Recognized as a security measure. |
Must meet standards ensuring data cannot be re-identified. | Allows processing of data while enabling re-identification if necessary. | |
PDPA (Personal Data Protection Act) - Singapore | Anonymized data is exempt from PDPA requirements. | Pseudonymization is acknowledged as a protective measure. |
Must ensure that anonymization is thorough and irreversible. | Pseudonymized data can still be considered personal data if re-identifiable. | |
POPIA (Protection of Personal Information Act) - South Africa | Encouraged for processing personal information in a manner that does not identify individuals. | Recognized as a method to protect personal information. |
Must ensure data cannot be re-identified. | Pseudonymization is subject to safeguards to prevent re-identification. | |
Personal Data Protection Bill - India | Anonymization is encouraged as a method to safeguard personal data. | Pseudonymization is recognized and encouraged for processing personal data securely. |
Must ensure data cannot be re-identified. | Re-identification controls must be in place. |
As data privacy regulations become more stringent and data breaches continue to pose significant risks, organizations are exploring innovative approaches to anonymize data effectively while maintaining its utility for analysis and research. One promising method gaining traction is synthetic data anonymization. Unlike traditional anonymization techniques that modify or remove real data, synthetic data involves generating entirely new datasets that mimic the statistical properties and relationships of the original data without containing any actual sensitive information.
Synthetic data anonymization leverages advanced statistical techniques and machine learning algorithms to create synthetic datasets that closely resemble the original data in terms of structure, patterns, and relationships. This process begins by analyzing the original dataset to understand its statistical properties and dependencies. Using this information, algorithms generate synthetic data points that are statistically similar to the real data but do not correspond to any actual individuals or entities. This ensures that the synthetic data cannot be linked back to specific individuals, providing a high level of privacy protection.
One of the main advantages of synthetic anonymization is its ability to preserve utility while ensuring privacy. Since synthetic datasets maintain the statistical characteristics of the original data, they can be used for complex analyses, machine learning model training, and other data-driven tasks without compromising individual privacy. It also reduces the risks associated with re-identification, as there are no real individuals' data to expose.
Synthetic data anonymization finds applications across various industries and domains. In healthcare, synthetic datasets can be used for medical research and algorithm development without accessing sensitive patient records directly. In finance, synthetic data enables robust risk assessment and fraud detection models without compromising customer privacy. Additionally, governments and research institutions utilize synthetic data to share insights and facilitate collaboration while adhering to strict data protection regulations.
Despite its potential benefits, synthetic data anonymization comes with challenges. Generating high-quality synthetic data that accurately reflects the original dataset's complexities requires sophisticated algorithms and careful validation. Ensuring that synthetic datasets do not inadvertently reveal patterns or information that could lead to re-identification is also critical. Furthermore, acceptance and validation of synthetic data by stakeholders and regulatory bodies may require establishing standards and benchmarks for evaluating its effectiveness and reliability.
As a privacy-first website intelligence provider, our platform fully anonymizes all data on collection in the default Maximum Privacy Mode, meaning that clients can leverage analytics in compliance with all global personal data protection laws - including both ePrivacy and GDPR.
However, we also understand that some businesses need access to personally identifiable information (PII). We’ve responded to this by also offering three lower-threshold data privacy modes. These keep progressively more personal information intact, providing clients with the data they need for user experience analytics, wider marketing work, and other initiatives.
And since data needs can vary from country to country, TWIPLA’s data collection - and the extent of anonymization - can be calibrated differently for any website visitor location of origin. This also makes it easy for clients to set local data anonymization to different legal jurisdictions, maximizing legitimate data capture while reducing compliance burdens.
Our advanced website intelligence solution will enable anyone to grow their website quickly, while protecting visitor data rights and driving up their ESG rating. Sign up for free today, remove your ugly cookie banner, and supercharge data collection!
And that’s it, that’s your introduction to the anonymization and pseudonymization of data, and the laws that control each method. It's fascinating technology that can help businesses to protect their customers while still being able to capitalize on the insights hiding within their data.
As privacy advocates, we regularly publish new content on the issues surrounding data protection, as well as digital analytics and wider marketing techniques. If you’d like to keep up with everything we release, the best way is to subscribe to our monthly newsletter. That way, you’ll receive a single email to your inbox each month that summarizes everything we’ve published in the last 30 days.
Gain World-Class Insights & Offer Innovative Privacy & Security
Keep pace with the world of privacy-first analytics with a monthly round-up of news, advices and updates!