Data Anonymization vs. Pseudonymization: Understanding the Key Differences

The terms might sound contemporary, but the development and use of anonymization and pseudonymization techniques date back to the 1960s and 70s. Still, there’s no denying that their widespread adoption has accelerated with a growing awareness of data protection and the rise of data privacy regulations. Worldwide organizations across various industries now increasingly employ these techniques as part of their data protection strategies and compliance efforts.

A relatively recent data breach that occurred before anonymization and pseudonymization were widely implemented is the 2006 AOL search data leak, when approximately 20 million search queries made by over 650,000 users over a three-month period were released. Even though the company had removed usernames and replaced them with unique identifiers, there was a significant amount of personally identifiable information (PII) still available for bad actors and others to exploit and re-identify individuals.

Unfortunately, those “others” were journalists and data researchers who had no problem determining individual users based on their search queries and other contextual information. The backlash, unsurprisingly, was quick and fierce.

If there was a silver lining to be found in this harmful breach, it was that it highlighted the need for more robust solutions like anonymization and pseudonymization to protect personal data. It also underscored the necessity for stricter privacy measures that would still allow legitimate data processing and analysis.

Anonymization vs. Pseudonymization

While both offer privacy protection, data security, and regulation compliance, anonymization and pseudonymization have one key distinction:

• Anonymization entirely removes the possibility of identification.
• Pseudonymization strikes a balance between privacy and data utility.

The anonymization process transforms personal data in such a way that it cannot be attributed to a specific individual, even by the data controller. It irreversibly removes any identifying information from datasets, ensuring the data can’t be linked back to an individual. Anonymized data is deemed outside the scope of data protection regulations, as it doesn’t fall under PII.

In contrast, pseudonymization replaces a dataset’s identifying information with pseudonyms or artificial identifiers. The original data might still be linkable to an individual, but to view it requires additional information that’s stored separately. The extra protective layer that separates sensitive information from the pseudonym makes it more difficult for anyone to directly identify individuals. Pseudonymized data is still considered personal data, as it can be linked to an individual with the help of additional information.

GDPR Anonymization vs. Pseudonymization

Anonymization and pseudonymization are recognized under the General Data Protection Regulation (GDPR) as effective techniques for protecting personal data, but each has its own definitions and implications:

1. Anonymization under the GDPR refers to the irreversible process of transforming personal data in such a way that it can no longer be attributed to an identifiable individual, even with additional information. As mentioned above, once data is anonymized, it is no longer considered personal data, meaning it falls outside the scope of the GDPR.
2. Pseudonymization is defined by the GDPR as the processing of personal data in such a way that it can no longer be attributed to a specific data subject with using additional information.

Both methods are encouraged in the GDPR. However, pseudonymization is often considered the better choice, as it makes data identifiable if needed but inaccessible to unauthorized users. In any event, GDPR requires organizations to take all appropriate technical and organizational measures to protect PII. Which you choose depends on factors like:

• Degree of risk of a data breach.
• The way your company processes data.
• The type of data your process.

Experts tend to agree that pseudonymization is the more sophisticated method for protecting data and meeting GDPR and other requirements, including HIPAA.

When to Choose Anonymization

You have various types of data anonymization at your disposal. A few common ones include:

1. Generalization, which involves replacing specific values, such as ages, with more general or broader categories, like age ranges.
2. Suppression or removal, where certain identifiers or sensitive attributes are wholly removed or suppressed from the dataset.
3. Noise addition introduces random disruptors or “statistical noise” to the data, adding variability to the values without significantly impacting the data’s overall statistical properties.
4. Data swapping or shuffling is the exchange or shifting of specific attributes or values within the dataset while maintaining consistency. The linkage between individual identifiers and their associated details is disrupted, making re-identification more difficult.
5. Data masking involves replacing sensitive or identifying values with fictional or artificial values while preserving the data’s structure and format.

These real-life scenarios illustrate the practical application of anonymization.

• Anonymization is critical in medical research to protect patient privacy while still allowing researchers to analyze large datasets. By anonymizing medical records, genetic data, and more, scientists can conduct studies on aggregated data without compromising individual privacy. The same holds true for clinical trials, where any patient data collected during the trial must be protected. Removing identifiable information like names, dates of birth, and medical record numbers keeps patients protected but allows researchers and pharmaceutical companies to analyze data without violating privacy regulations.
• Anonymization is also relevant in the financial sector, particularly when institutions must share or analyze customer data for purposes like risk assessment, market analysis, or fraud detection. Anonymizing financial data by replacing PII like names, addresses, and account numbers with artificial identifiers permits data analysis while protecting customer privacy.

When Pseudonymization is the Better Choice

Data pseudonymization also has several approaches, including:

1. Deterministic pseudonymization, which uses a consistent and fixed algorithm to transform identifiable data into pseudonyms.
2. Randomized pseudonymization, where pseudonyms are generated using random strings or cryptographic methods.
3. Tokenization, a technique where PII elements are replaced with randomly generated tokens.
4. Format-preserving pseudonymization, which retains the original data’s format and structure but replaces sensitive values with pseudonyms.

The approach you choose depends on specific use cases, regulatory requirements, and the desired level of protection.

For instance, financial institutions can use pseudonymization in their CRM systems to manage customer relationships while protecting sensitive personal information. Pseudonyms or artificial identifiers are applied to customer profiles, separating out original identifying information such as names or social security numbers and storing them in a secure location. Lenders, investment firms, and fintech companies can then analyze customer data for marketing purposes or customer behavior analysis while maintaining individual privacy individuals.

Credit card companies, retailers, and insurance companies find pseudonymization helpful in fraud detection, ensuring the original identities of individuals remain protected while allowing the detection of patterns or anomalies that may indicate fraudulent activities.

One valuable medical use case for pseudonymization is electronic health records (EHR). By assigning pseudonyms to patient records, the original PII, such as names, social security numbers, or health insurance information, can be separated from medical data, enabling healthcare providers to securely store and share patient information for treatment, research, or statistical analysis without risking unauthorized access to sensitive data.

Velotix’s AI-powered data governance platform streamlines and strengthens data privacy practices. It leverages advanced algorithms and techniques to effectively anonymize and pseudonymize sensitive data, ensuring compliance with regulations and mitigating privacy risks.

There’s no better way to instill trust and demonstrate your organization’s commitment to privacy than by investing in technologies that help you take control of your data and position your company as a leader in data protection.