Data Anonymization: How to Share Sensitive Information While Complying with the GDPR

Sharing data with third parties sounds straightforward—until that data includes information that can identify individuals. A hospital collaborating with a research center, an energy company analysing consumption patterns, or a human resources department sharing workforce indicators with an external consultancy all face the same challenge. They need to share data, but they cannot risk exposing personal information without adequate safeguards.

This is where data anonymization stops being an abstract compliance concept and becomes a practical technical requirement. If you are a Data Protection Officer (DPO), a compliance manager, or part of a data team, you probably already know that the General Data Protection Regulation (GDPR) does not prohibit the sharing of personal data. Instead, it requires robust safeguards whenever such sharing takes place. The real question is how to implement those safeguards without stripping the data of its analytical value.

In this article, we review the most widely used techniques for anonymizing data before sharing it—k-anonymity, generalization, and differential privacy—using examples from healthcare, energy consumption, and human resources. We also explain how a modern data space can automate this process through dedicated data applications.

Why Data Anonymization Is Different from Pseudonymization

Before looking at specific techniques, it is important to clarify a common misconception.

Pseudonymization replaces direct identifiers (such as names, ID numbers, or medical record numbers) with codes. However, provided that the appropriate key is available, individuals can still be reidentified. Under the GDPR, pseudonymized data remains personal data.

Anonymization, on the other hand, aims to make reidentification practically impossible—even when the dataset is combined with other available information. When effective anonymization is achieved, the resulting dataset falls outside the scope of data protection legislation, significantly expanding the possibilities for sharing and analytical use.

This distinction is not merely theoretical. It determines which data-sharing agreements an organization can establish within a data space and which controls must be verified before accepting a dataset from another participant.

k-Anonymity: The Most Widely Used Technique

k-Anonymity is probably the best-known anonymization technique and one of the most practical for business and public-sector environments. Its principle is simple: every combination of indirect identifying attributes—such as age, postal code, gender, or job category—must appear at least k times within the dataset.

If k = 5, every record becomes indistinguishable from at least four other records sharing the same quasi-identifiers. Increasing the value of k makes reidentification more difficult, although it also reduces the level of detail preserved in the data.

Healthcare Example

Imagine a healthcare provider in Castilla y León that wants to share patient data with a research group studying the progression of a chronic disease. The original dataset contains exact age, full postal code, and diagnosis date.

Applying k-anonymity with k = 5, the system would:

• Replace exact ages with five-year age ranges.
• Reduce the postal code to its leading digits.
• Group diagnosis dates by quarter.

As a result, every combination of these attributes appears in at least five different patient records, making it significantly more difficult to isolate a specific individual while still preserving sufficient information for epidemiological analysis.

Energy Consumption Example

An electricity supplier operating in Castilla y León may wish to share aggregated consumption curves with an energy-efficiency consultancy.

Without anonymization, hourly consumption profiles could often be linked to individual households, particularly in sparsely populated rural areas where only a few homes share similar consumption patterns.

Applying k-anonymity groups households into clusters based on consumption ranges and broader geographical areas. Individual consumption profiles can no longer be attributed to a specific property, while analysts can still identify meaningful consumption patterns across different regions.

Generalization: Reducing Precision While Preserving Value

Generalization is often used alongside k-anonymity. Rather than removing information entirely, it reduces its level of precision.

Instead of storing an exact value, the dataset stores a broader category that remains useful for analysis.

Typical examples include:

• Exact age → Age range (30–35)
• Full address → City or region
• Specific salary → Salary band
• Exact date → Month or year

This approach preserves statistical value while significantly reducing the likelihood of reidentification.

Human Resources Example

Imagine a company that wants to share workforce diversity data with a university research project.

The original dataset includes:

• Exact age
• Department
• Years of experience
• Salary
• Job title

Instead of publishing exact values, the anonymization process could transform the information into:

• Age groups
• Experience ranges
• Salary bands
• Broader professional categories

Researchers would still be able to analyse salary gaps, career progression, and diversity indicators without exposing information about any individual employee.

Differential Privacy: Mathematical Protection Against Reidentification

Although k-anonymity is highly effective in many scenarios, it has limitations when datasets are repeatedly queried.

This is where differential privacy provides an additional layer of protection.

Rather than modifying the original dataset directly, differential privacy adds carefully calibrated statistical noise to the query results.

The objective is that the inclusion—or exclusion—of a single individual has a negligible impact on the final output.

This means analysts obtain statistically reliable information while making it virtually impossible to infer whether a specific person was included in the dataset.

Differential privacy has become the preferred approach for large-scale analytics and is already used by organisations such as:

• Apple
• Google
• Microsoft
• The U.S. Census Bureau

Healthcare Example

A hospital wants to publish the number of diabetes patients by municipality.

Instead of releasing the exact counts, differential privacy introduces a small amount of controlled noise:

• Actual value: 127 patients
• Published value: 129 patients

For population-level analysis, the difference is insignificant.

However, for someone attempting to determine whether a particular individual appears in the dataset, the uncertainty introduced by the statistical noise provides strong privacy guarantees.

Environmental Monitoring Example

Suppose several organisations within a data space share environmental measurements to analyse air quality across a region.

Differential privacy makes it possible to publish aggregated pollution indicators while preventing the identification of measurements originating from a specific monitoring station or participant.

Researchers still obtain reliable trends, seasonal patterns, and predictive models without exposing sensitive operational information.

Choosing the Right Technique

There is no single anonymization method that fits every scenario.

The most appropriate approach depends on:

• The type of data.
• The intended analytical purpose.
• The acceptable privacy risk.
• The applicable legal and regulatory requirements.

In practice, organisations often combine multiple techniques.

For example:

• Generalization reduces data granularity.
• k-Anonymity ensures each record is indistinguishable from several others.
• Differential privacy protects statistical queries and published results.

Combining these methods provides stronger privacy guarantees while maintaining the usefulness of the data.

How a Data Space Automates Anonymization

One of the biggest challenges in data anonymization is not the anonymization techniques themselves, but applying them consistently across multiple datasets and organizations.

Modern data spaces address this challenge by integrating anonymization directly into data pipelines.

In our data space, anonymization is implemented as a processing application within the pipeline.

The workflow is straightforward:

1. A data provider selects the dataset to be shared.
2. The dataset passes through an anonymization application.
3. The application applies the configured techniques, such as k-anonymity, generalization, or differential privacy.
4. The anonymized dataset is transferred to the consumer according to the agreed data-sharing policy.

This approach ensures that sensitive data never leaves the provider's infrastructure without first undergoing the required protection measures.

Because anonymization is part of the automated pipeline, organizations avoid repetitive manual processes while ensuring consistent compliance across every data-sharing operation.

Privacy by Design, Not as an Afterthought

The GDPR introduces the principle of Privacy by Design, meaning privacy protections should be embedded into systems from the outset rather than added later.

A data space follows this principle naturally.

Data sharing is governed by:

• Verified agreements between participants.
• Usage policies defining how data may be processed.
• Secure data transfer mechanisms.
• Complete traceability of every operation.
• Automated anonymization before any data exchange.

This architecture enables organizations to collaborate without compromising confidentiality or regulatory compliance.

Sharing Data Responsibly

Data anonymization is not about making data unusable.

It is about preserving its value while protecting the individuals behind it.

Healthcare providers, industrial companies, public administrations, research institutions, and many other organizations can collaborate far more effectively when anonymization becomes an integral part of the data-sharing process rather than an additional manual task.

Within a modern data space, anonymization is no longer an isolated operation. It becomes one step in a secure, automated, and auditable workflow that allows organizations to unlock the value of their data while maintaining full control over privacy and regulatory compliance.

The result is a trusted environment where information can be shared safely, innovation can flourish, and organizations remain aligned with European principles of data sovereignty, security, and responsible data governance.