Anonymized data: what is it and how to treat it?
Posted: Sun Dec 22, 2024 5:02 am
Growing awareness of the importance of privacy and data security has brought to the fore the need to balance the usefulness of data with the protection of identity and personal information.
It is in this context that anonymized data emerges as an essential solution, as it enables organizations to leverage the benefits of data without compromising privacy. But how exactly does anonymization work, and what are the best practices for implementing it?
In this article, we will explain the techniques and challenges involved in the anonymization process, demonstrating how this data is treated responsibly and effectively. Stay tuned!
What is anonymized data?
According to the General Data Protection Law (LGPD) , anonymized data is that which was originally related to a specific person, but which went through a careful process to ensure that it could no longer be linked to that person.
To understand this in a more practical way, consider the following example:
Imagine that a market analysis company has collected information about a customer's purchase history containing highly personal data, including:
Name;
Address;
Profession;
CPF;
Transaction details.
However, in order to use them ethically and in australia business mailing list compliance with the LGPD , the company decides to anonymize them.
In this process, all directly identifiable information, such as names and addresses, is removed. What’s left are just records of transactions, dates, and amounts, with no explicit link to a specific individual. This is where the magic of anonymization happens. Even if the original data was highly personal, after anonymization, it becomes aggregates of useful information that cannot be traced back to a particular individual.
What is the difference between anonymization and pseudo-anonymization?
When addressing data protection and privacy , it is essential to understand the difference between anonymization and pseudo-anonymization, two concepts that play distinct roles in preserving the confidentiality of personal information.
As we have detailed previously, anonymization is the process by which original data is altered so that it can no longer be linked to a specific individual , even with additional information.
This is usually achieved by removing or replacing direct identifying details such as names, addresses and personal identification numbers.
On the other hand, pseudo-anonymization involves replacing direct identification information with keys or codes, making the data less identifiable but still linked to a specific entity or person, such as biometric access.
In practice, pseudo-anonymized data maintains a degree of reversibility within a secure and controlled environment, because if someone has access to the appropriate decryption keys, it is possible to link the data to an individual. This can be used in situations where reversibility is necessary for internal processes or analysis , but data privacy must still be protected.
Understand how anonymized data is handled
The treatment of anonymized data follows a meticulous process that combines these techniques to make the data inaccessible to the identification of individuals, while maintaining its usefulness for analysis and research.
These practices include data suppression, generalization, adding noise to data, and aggregation. Let’s explore each of them in detail:
1. Data deletion
Redaction involves removing personally identifiable information from the original data , such as names, identification numbers, and addresses.
The aim is to eliminate any trace of personal identification in a database, making them anonymous and inaccessible for the identification of individuals.
2. Generalization
Generalization consists of replacing specific information with broader categories or ranges in order to maintain the usefulness of the data in an impersonal way.
For example, instead of recording exact ages, data can be generalized to age ranges (e.g., 20-30 years, 31-40 years). This way, the identity of individuals is preserved, while keeping the data useful for analysis.
3. Adding noise to the data
Noise-splicing involves introducing false or inaccurate information into data in a controlled manner . This processing technique makes it more difficult to identify specific individuals and improves data protection , especially in statistical analyses.
It is in this context that anonymized data emerges as an essential solution, as it enables organizations to leverage the benefits of data without compromising privacy. But how exactly does anonymization work, and what are the best practices for implementing it?
In this article, we will explain the techniques and challenges involved in the anonymization process, demonstrating how this data is treated responsibly and effectively. Stay tuned!
What is anonymized data?
According to the General Data Protection Law (LGPD) , anonymized data is that which was originally related to a specific person, but which went through a careful process to ensure that it could no longer be linked to that person.
To understand this in a more practical way, consider the following example:
Imagine that a market analysis company has collected information about a customer's purchase history containing highly personal data, including:
Name;
Address;
Profession;
CPF;
Transaction details.
However, in order to use them ethically and in australia business mailing list compliance with the LGPD , the company decides to anonymize them.
In this process, all directly identifiable information, such as names and addresses, is removed. What’s left are just records of transactions, dates, and amounts, with no explicit link to a specific individual. This is where the magic of anonymization happens. Even if the original data was highly personal, after anonymization, it becomes aggregates of useful information that cannot be traced back to a particular individual.
What is the difference between anonymization and pseudo-anonymization?
When addressing data protection and privacy , it is essential to understand the difference between anonymization and pseudo-anonymization, two concepts that play distinct roles in preserving the confidentiality of personal information.
As we have detailed previously, anonymization is the process by which original data is altered so that it can no longer be linked to a specific individual , even with additional information.
This is usually achieved by removing or replacing direct identifying details such as names, addresses and personal identification numbers.
On the other hand, pseudo-anonymization involves replacing direct identification information with keys or codes, making the data less identifiable but still linked to a specific entity or person, such as biometric access.
In practice, pseudo-anonymized data maintains a degree of reversibility within a secure and controlled environment, because if someone has access to the appropriate decryption keys, it is possible to link the data to an individual. This can be used in situations where reversibility is necessary for internal processes or analysis , but data privacy must still be protected.
Understand how anonymized data is handled
The treatment of anonymized data follows a meticulous process that combines these techniques to make the data inaccessible to the identification of individuals, while maintaining its usefulness for analysis and research.
These practices include data suppression, generalization, adding noise to data, and aggregation. Let’s explore each of them in detail:
1. Data deletion
Redaction involves removing personally identifiable information from the original data , such as names, identification numbers, and addresses.
The aim is to eliminate any trace of personal identification in a database, making them anonymous and inaccessible for the identification of individuals.
2. Generalization
Generalization consists of replacing specific information with broader categories or ranges in order to maintain the usefulness of the data in an impersonal way.
For example, instead of recording exact ages, data can be generalized to age ranges (e.g., 20-30 years, 31-40 years). This way, the identity of individuals is preserved, while keeping the data useful for analysis.
3. Adding noise to the data
Noise-splicing involves introducing false or inaccurate information into data in a controlled manner . This processing technique makes it more difficult to identify specific individuals and improves data protection , especially in statistical analyses.