In the digital age and technological advancement we live in, data has become a valuable asset that is essential for making decisions in our daily lives. Activities such as registering on websites, obtaining a bank card, or enrolling in social security require our personal data. This growing reliance on data has raised concerns about privacy and the protection of personal information.

In this context, the need to protect the privacy of personal data has become a challenge for companies, as they share a significant amount of information for analysis and testing. It is important to highlight that the risks of exposing this data are growing exponentially due to the increase in cyberattacks (+7% compared to 2022) and human errors, which account for 95% of sensitive information leaks.

The anonymization of structured data offers a balance between privacy and the utility of personal data. Despite being protected from potential exposure and malicious use, the data remains useful. In this way, companies can make ethical and legal use of information, avoiding penalties and reputational damage.


Anonymization of structured data is a set of techniques and processes used for the removal or modification of personal data, preventing the identification of individuals behind the information. Structured data refers to data organized in a predefined format, such as a table, a text file, or a database, with defined attributes and fields.

The objective of anonymizing structured data is to protect the privacy and confidentiality of personal information while maintaining the utility of the data for analysis and use in different applications. When anonymizing data, attributes that could directly identify a person, such as names, addresses, personal identification numbers, etc., are removed or modified.

There are different anonymization techniques applicable to structured data, such as deletion, consistent tokenization, or substitution with synthetic data. These techniques are applied depending on the intended use of the protected data. The following section details each of the techniques, as well as the use cases where their application is crucial.


There are different techniques for masking structured data, each with its own advantages and disadvantages depending on the intended use of the protected data.


This technique involves completely removing personal data from records. Instead, asterisks are included to indicate that there was originally personal data in that field. This is an effective technique that ensures non-identification of individuals. However, replacing with asterisks or strikethroughs can sometimes complicate the readability and analysis of the information. Therefore, this technique is appropriate for cases where data protection is the primary goal.

Substitution with synthetic data

This technique involves replacing personal data with fictitious data of the same nature. This allows personal data to remain in the same format but be unrecognizable to those without authorized access. This technique facilitates the understanding and analysis of the information by replacing the data with others of the same nature, rather than using asterisks. Therefore, this technique is useful for use cases where, in addition to protecting the data, maintaining the format or context of the information is important for analysis, research, etc.

Consistent tokenization

This technique replaces data with consistent tokens composed of a prefix indicating the information type (PER, LOC, WEB, DAT) and a numerator (to differentiate them). This way, the individual’s name is replaced with the word PER, preventing the person’s identification. This technique safeguards the individual’s privacy and maintains the value and readability of the information. Unlike the technique that substitutes originals with synthetic data, tokenization allows quick identification of data users, as we are dealing with a sample of protected data.


Anonymization of structured data is one of the most common techniques used by companies to protect the privacy and confidentiality of information. Here are use cases where anonymization of structured data is employed:

Cloud data protection: Storing data in the cloud is increasingly common. Companies have databases, CSV files, and spreadsheets containing confidential information and personal data used for various processes. Anonymization of structured data protects privacy, ensures the success of company processes, and prevents potential exposure in the event of data breaches.

Regulatory compliance: Anonymization of structured data helps companies comply with data protection regulations and laws, such as the General Data Protection Regulation (GDPR) in the European Union. Protecting the privacy of identifying data prevents information exposure, avoiding potential fines and penalties for violating data protection regulations.

Sharing database files: In the healthcare sector, it is common to share information from organizational databases for research purposes. Therefore, it is necessary to anonymize personal data and maintain the confidentiality of patient identities. Anonymization of structured data removes relevant identifiers to protect the privacy of personal information that needs to be shared with third parties.

Development and software testing: In development and testing environments, production data sets are commonly used to test applications and new features. Anonymization of structured data ensures that data used in tests does not contain personally identifiable information, protecting privacy and avoiding exposure to security breaches. Development and testing environments are especially vulnerable to external attacks, making working with protected data crucial in minimizing risks.

Data analysis and testing: Companies and organizations use personal data for process analysis or study development. Anonymization of structured data protects the nature of this data while maintaining the structure and context of structured files for accurate usage. This is particularly relevant when working with large data sets containing sensitive information from which insights or patterns need to be extracted.


Anonymization of personal data helps meet data protection regulations such as ISO 270001 and the National Security Scheme (ENS). These regulations aim to ensure the security of sensitive information in public administrations, organizations, and both the private and public sectors.

Through anonymization of structured data, compliance with ISO 27001 requirements, such as access control, is simplified. By anonymizing structured data, there is reduced need to assign specific access permissions to individual users, as sensitive information is no longer present in the anonymized data. This simplifies and enhances data access control. Additionally, it simplifies the management of information security incidents (A.16.1.5). If a security incident occurs in the organization, prior anonymization of the data can help minimize the impact and consequences for the data subjects involved.

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In conclusion, using this data protection technique contributes to simplifying compliance with regulatory requirements established by various norms and regulations. However, the benefits of applying anonymization to structured data go beyond regulatory compliance.

Using this technique simplifies structured information management within companies, as it protects data without the need to create backups for various applications. The absence of duplicated information is undoubtedly a relevant feature for information management carried out by CISOs and CIOs in organizations.

In general, anonymization of structured data offers crucial benefits in terms of privacy, security, regulatory compliance, and responsible data use.

Nymiz helps protect the confidentiality of personal data

Nymiz mitigates the consequences of security breaches in databases and structured files. Through the use of artificial intelligence, Nymiz can contextually detect personal data to anonymize it, ensuring protection against potential threats and data leaks. Moreover, it maintains the utility and integrity of information, enabling its use in advanced analytics, testing environments, or research projects.

Nymiz simplifies the challenge of anonymizing databases by automating the process and avoiding the creation of duplicated information that complicates data governance.

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