written by Willian de Vargas
6 minutes reading
Cases of data breaches, such as the so-called "Mother of All Breaches" (MOAB) revealed in January 2024 and which exposed 26 billion user records worldwide and the cyberattack on Repsol, a Spanish multinational in the energy sector, which compromised the customer database in Spain, highlight the magnitude of the risk. These incidents expose millions of people to threats such as financial fraud, identity theft, and reputational damage.
Given this scenario, the protection of sensitive information has become a priority for individuals, companies, and governments. Data such as ID numbers, medical records, financial information, and behavioral patterns can be misused if they fall into the wrong hands, with consequences ranging from loss of credibility for digital services to irreparable financial damage.
It is in this context that data anonymization emerges as a solution to balance the strategic use of information with privacy preservation. This technique allows organizations to use data for analysis, research, and technology development without compromising individuals’ identities.
But after all, what is data anonymization? How can it be applied effectively? And what are the main challenges involved?
Data anonymization is the process of transforming personal information in an irreversible way, making it impossible to identify the individual to whom the data belongs. Unlike pseudonymization, which merely replaces direct identifiers with pseudonyms, anonymization ensures that, even through data cross-referencing, it is not possible to reverse the process.
A practical example is data masking, such as partially removing ID numbers or replacing names with random characters in a database. Thus, even in the event of a breach, the usefulness of this data for illicit purposes is considerably reduced.
Protecting sensitive information is essential to preserve individuals’ privacy and avoid financial losses, reputational damage, and legal risks. From a legal and ethical standpoint, Brazil’s General Data Protection Law (LGPD) requires appropriate measures to ensure the privacy and security of personal data. Anonymization stands out as an effective strategy that allows companies to remain compliant with legislation while safely using data for innovation and analysis.
User privacy: Protects personal data, reducing risks of exposure and misuse.
Legal compliance: Helps companies meet the requirements of the LGPD and other regulations.
Protection against breaches: Even in cases of unauthorized access, anonymized data does not allow for individual identification.
Research and innovation: Enables the use of large volumes of data in studies and technology development without compromising privacy.
Internal security: Allows information sharing among internal teams without exposing sensitive data.
There are several techniques that can be used for data anonymization, depending on the required protection level and the purpose of data use.
Generalization: Reduces data specificity, making it less identifiable. This can be done by grouping information into broader categories or removing specific details. A good example is converting an exact birth date, such as 10/26/1996, into an age range (25–30 years) or just the birth year (1996). This method increases privacy while maintaining data usability.
Masking: Replaces original values, totally or partially, with generic or random characters to prevent direct identification. It is often used to hide sensitive data, for example, changing a CPF number from 123.456.789-10 to 123..-**, or a credit card number to **** **** **** 1234. This technique is useful for displaying data to users without full access.
Perturbation: Slightly modifies original data by introducing small variations, such as statistical noise or rounding, making identification harder without compromising statistical analysis results. For instance, changing a salary value from R$ 5,283.00 to R$ 5,300.00 preserves usefulness in studies without revealing exact figures.
Tokenization: Replaces sensitive information with tokens (unique identifiers) that have no direct link to the original data. These tokens can only be reverted through a secure system that stores them separately. It is widely used in financial transactions and payment systems, such as replacing credit card numbers with temporary codes for online purchases. For example, replacing a bank account number like 123654789 with a token A1B2C3D4 ensures that intercepted data cannot be used without access to the system that performs the conversion.
Access control: Although not strictly an anonymization method, it is an essential practice for protecting sensitive data. It defines access permissions based on authorization levels, ensuring that only authorized users can view or manipulate specific information. This approach complements other methods by reducing the risk of improper exposure. For example, in medical record systems, only doctors and nurses can access a patient’s full health history, while administrative staff may only see basic details such as name and appointment date.
Anonymization is widely applied across sectors to ensure data security while enabling its use for various purposes:
Healthcare: Protecting medical records and patient histories in clinical research.
Finance: Analyzing transaction patterns without exposing banking details.
Marketing: Personalizing advertising campaigns without compromising individual data.
Public sector: Analyzing census and population statistics while preserving citizens’ privacy.
Education: Conducting academic research using anonymized databases.
As we can see, data anonymization is an essential tool for protecting privacy and ensuring information security in the digital world. Below, we explore a case study of this technique in the healthcare sector, the MIMIC database and the challenges faced in implementing data anonymization.
A great example of applying data anonymization techniques to sensitive data is the MIMIC (Medical Information Mart for Intensive Care) biomedical database, developed by the Massachusetts Institute of Technology (MIT) in partnership with the Beth Israel Deaconess Medical Center and made available through the PhysioNet platform.
Since its creation, MIMIC has evolved significantly: the first version, MIMIC-I, was released in 2003 with ICU admission data from a single hospital. Later, MIMIC-II introduced improvements in standardization and support for retrospective studies; MIMIC-III, launched in 2015, greatly expanded the dataset, including over 60,000 ICU admissions and de-identified clinical notes, enabling research with Natural Language Processing (NLP).
Currently, the database is in its MIMIC-IV version, published in 2024, featuring a modular and modern structure that encompasses anonymized clinical data from approximately 315,000 patients, totaling more than 524,000 hospital admissions between 2008 and 2019 at Beth Israel Deaconess Medical Center.
MIMIC-IV has two main modules: the HOSP module, containing administrative and general clinical data such as lab results, prescriptions, procedures, and clinical notes; and the ICU module, which concentrates granular data collected during intensive care unit stays, including near-real-time monitored vital signs.
In total, MIMIC-IV comprises 31 tables and over 600 million records, making it one of the largest public biomedical datasets in the world. One major addition in MIMIC-IV is the inclusion of emergency department records, which greatly expand analytical possibilities for studying clinical trajectories, from patient admission to final outcomes.
This database is widely used in academic and scientific research worldwide, serving as a valuable resource for developing machine learning models, epidemiological studies, clinical hypothesis testing, and medical protocol validation. Its relevance stems not only from the richness and granularity of the data but also from being one of the few public datasets offering this level of detail in biomedical data, contributing directly to advances in evidence-based medicine.
Given the sensitivity of the information contained in MIMIC involving real patient data, anonymization plays a fundamental role in its public availability. The team responsible for database curation follows a rigorous de-identification process, adhering to international privacy standards such as the Health Insurance Portability and Accountability Act (HIPAA), a U.S. law regulating the use and disclosure of protected health information.
In this anonymization process, personal data such as names, phone numbers, addresses, government ID numbers, and other direct identifiers are completely removed or replaced with random codes. For example, each patient’s medical record number is replaced by an anonymous identifier called subject_id, which allows safe linkage across tables without revealing the patient’s identity.
Additionally, generalization and temporal obfuscation techniques are applied. A classic example is date shifting: admission, discharge, test, and procedure dates are shifted by a random number of days, consistent within each patient but different between patients. This preserves the sequence and duration of clinical events (e.g., “discharge after 5 days of hospitalization”) but makes it impossible to determine the real dates. For instance, an admission that originally occurred in March 2016 might appear as July 2014 in the anonymized dataset.
Another practical example involves patient age: individuals aged 90 or older are grouped into a single category, “90 or above.” This prevents reidentification of very elderly patients, whose demographic profiles may be rare and thus more easily identifiable even after removing other data.
Finally, restriction and access control techniques are also applied. Unlike open public databases, access to MIMIC requires completion of a research ethics course and signing a data use agreement, ensuring that researchers use the information only for scientific purposes and with proper respect for individual privacy.
The healthcare field, however, presents significant challenges to anonymization. Biomedical data are often highly granular and interrelated, which may facilitate patient reidentification even after direct identifiers are removed. Variables such as rare diagnosis combinations, admission dates, and specific treatments can inadvertently allow identification. Therefore, anonymization in this sector must carefully balance privacy protection and data utility for research.
The use of MIMIC demonstrates how anonymization, when applied with technical rigor and ethical responsibility, can enable access to sensitive clinical data without compromising patient privacy. This practice not only protects individual rights but also drives scientific and technological progress that benefits society as a whole.
Reidentification risk: Data combinations or cross-referencing with other datasets can expose identities.
Loss of utility: The more anonymous the data, the higher the risk of losing analytical value.
Technical complexity: Each case requires specific techniques to balance protection and functionality.
Legal compliance and cost: Implementing high-quality anonymization requires investment and strategic planning.
Data anonymization is one of the most important tools for protecting privacy in a data-driven world. With the advancement of technologies such as artificial intelligence and machine learning which depend on large volumes of information, balancing privacy and innovation has become an increasingly urgent challenge.
More than a legal requirement, anonymizing data is an ethical choice. It means respecting the individuals behind the information and ensuring a safer, more transparent, and sustainable digital environment.
The future of anonymization depends on the ability to combine technical rigor with human sensitivity. Only then will it be possible to promote responsible data use and drive progress that benefits society as a whole.
