The anonymization of personal data represents a set of techniques and methods used to guarantee the right to privacy of individuals against the use of their personal data by third parties. It is a methodology that minimizes the risks of an individual’s re-identification.
It was created as a product of the natural evolution of cybersecurity methods. In the face of today’s increasing technological progress, which involves the mass use of data, anonymization also presents its latest developments.
We live in an era where sharing information and personal data has become routine, in which sensitive and private information must be handed over in every process or activity in which the citizen is involved.
Moreover, technology is evolving and becoming more capable of capturing, processing, and even sharing data.
Above all, the anonymization of personal data is important, necessary, and urgent to guarantee the privacy and fundamental rights of individuals, through altering, changing, or eliminating data that can be used to identify them, protecting all confidential information and preventing the anonymized information from being used for illicit purposes.
Outlined below are some of the various techniques for obtaining anonymized personal data:
Data generalization. This is a technique through which certain data sets are deleted to achieve anonymized information with less possibility of identification.
Data exchange. This is one of the techniques in which anonymized data are more difficult to re-identify. It simply consists of mixing the data, thus creating a record that is completely different from the original data set.
Data masking. This technique hides certain real data. It consists of randomly creating different versions of the same data set and then mixing them with the original set of information.
Pseudonymization. This consists of replacing the identifying data with other false data (pseudonyms). It is one of the techniques that safeguards the value of anonymized personal data for statistical analyses.
Synthetic data. In this technique, sets of data are created using algorithms based on statistical models, always starting from the real data to be anonymized.
It is clear that anonymization tools and techniques disassociate identifiers (names, address, date of birth, etc.) from personal data, making individuals unidentifiable. In the current era, however, these technologies must evolve to face the information giant, big data.
Why? Because with the accumulation of so much information about people in the world, there is a latent risk that the anonymized data will end up coinciding with certain information that can be used to re-identify individuals.
The solution is also not to go to extremes in the anonymization process either, because it increases the risk that the data will lose their usefulness for the required analysis.
Faced with this dilemma, anonymization technology is evolving towards personalized methods, according to the purposes for which the information is used and the characteristics of the personal data.
One of these technologies is Pk-anonymization, a method that manages to process individual data in such a way that they are changed probabilistically. Pseudo-personal data is thus obtained but based on real personal information.
First, the personal data are processed to identify them by the probability equal to or less than 1/k. Then, the original state of the information is estimated, using a statistical inference (Bayesian inference) based on machine learning.
Related content: The Best Data Anonymization Tools and Techniques
As discussed above, the record of personal information nowadays is immense and technologies such as artificial intelligence, blockchain, or big data are developing a fundamental role in every activity of the human being.
This datafication is more intense in the metaverse, a virtual environment that seeks to amplify the experience of human beings in every aspect of their lives (personal, social, political, and economic), through the use of avatars. All types of personal data are collected for this purpose, including biometric data and non-verbal information such as behavioral information.
Thus, the metaverse implies an operating mode in which an enormous volume of information about the individual and all the activities and relationships associated with them is used, which poses an exponential risk to privacy rights.
Consequently, mass data processing in the metaverse must be regulated by the provisions of the General Data Protection Regulation (GDPR) so that privacy is guaranteed through novel personal data anonymization techniques that preserve the privacy of the avatar, fingerprint, and other biometric data. Another purpose, moreover, is to avoid the re-identification of anonymized data in the face of such a vast universe of information.
Given the large collection, storage, and use of identifying data, whether direct or indirect, it becomes a business necessity to apply anonymization techniques, not only to eliminate personal data, but also to eliminate any data that, through associations, can be used to identify the individual.
At Pangeanic, we have developed a complete personal data anonymization service in which we offer several processes to destroy electronic traces that may facilitate the misuse of information.
Our anonymization software is especially for electronic archives used by banks, financial institutions, hospitals, insurance companies, legal experts, public administration institutions, and for any company that requires the use of anonymized personal data.
Contact us. We help you to safeguard the privacy of your customers and keep your company’s reputation high.