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In the digital age, where data processing and analysis are essential for decision-making, the privacy of personal information has become a key concern. Often, even if a database does not contain direct identifiers of individuals, it is possible to trace their identity by cross-referencing information with other related databases. This risk of re-identification poses a significant threat to the privacy of data subjects whose information is being processed.

The Challenge of Pseudonymization and Indirect Identification

The General Data Protection Regulation (GDPR) states that pseudonymized personal data still constitutes identifiable information if it is possible, with reasonable effort, to associate it with a natural person. This effort may depend on factors such as access to other databases, available resources, and technological advancements.

For example, imagine a database storing clinical information without including names or identity documents but maintaining a medical record number. If this number is linked to another hospital database that associates medical records with names, identifying the patient becomes feasible.

Anonymization: A Challenge in Data Protection

The process of anonymizing a database involves removing direct identifiers (such as names or ID numbers) and retaining only the data necessary for analysis, such as birth date, place of residence, or gender. However, even after this procedure, combining this data with other sources may enable the re-identification of individuals.

Those attributes that are not direct identifiers but, when combined, can reveal a person’s identity are called quasi-identifiers or indirect identifiers. The possibility of using these combinations to identify someone represents a risk of de-anonymization that must be effectively managed.

Statistical Disclosure Control and K-Anonymity

To mitigate the risk of re-identification, techniques have been developed within the discipline known as Statistical Disclosure Control (SDC). These techniques aim to maximize privacy without compromising data utility.

One of the most widely used strategies in this context is K-Anonymity. This methodology ensures that, within a dataset, each combination of quasi-identifier attributes appears at least k times. In other words, each person is indistinguishable from at least k-1 other individuals.

How K-Anonymity Works

A dataset is considered k-anonymous if each combination of quasi-identifier attributes appears in at least k distinct records. For example, if a dataset is 5-anonymous, it means that each combination of age, postal code, and gender appears in at least five records, making individual identification more difficult.

Practical Example

Let’s consider a database with the following attributes: Age, Postal Code, Gender, and Disease.

Age	Postal Code	Gender	Disease
34	28001	M	Diabetes
34	28001	M	Hypertension
34	28001	M	Cancer
34	28001	M	Asthma
34	28001	M	Flu

In this case, the dataset is 5-anonymous, as each combination of age, postal code, and gender appears in at least five records, preventing individual identification.

Advantages and Limitations of K-Anonymity

Advantages:

✅ Protects privacy without completely eliminating the utility of the data.

✅ Facilitates data sharing safely for studies and research.

✅ Provides an objective metric to assess the degree of anonymity in a database.

Limitations:

❌ Loss of precision: Generalizing data may affect its quality and analytical value.

❌ Vulnerability to certain attacks: K-anonymity does not protect against homogeneity attacks (when all records in a k-anonymous group have the same sensitive attribute) or background knowledge attacks (when an attacker has prior information about certain individuals).

K-anonymity is an essential technique for protecting personal data in a context where privacy is threatened by database access and cross-referencing. However, its application must be complemented with other measures to ensure effective protection against individual re-identification.

In a world where data has become one of the most valuable assets, balancing privacy and data utility is a constant challenge. Implementing techniques such as k-anonymity, along with a proactive approach to risk management, is key to ensuring that data-driven innovation does not compromise people’s privacy.