Today, the value of data represents a large part of the turnover of tech companies, in France alone it represents the sum of more than 2.7 billion euros and a growth of more than 4% is expected in the years to come (1). It is therefore important to know how to process this data and especially to protect user data. Data protection is essential, on the one hand, to comply with the various laws governing the valorization of data and, above all, to protect the information they contain against computer attacks. In 2023, there was a 30% increase in computer attacks compared to the previous year (2). As far as data breaches are concerned, in 2022 there were 781 victims in Europe, then the number jumped by 52% in 2023 with 1186 attacks (3).
In this article we will show you two techniques that protect sensitive data: tokenization and anonymization. We will then study two use cases and see which solution is the most suitable in these two scenarios.
What is tokenization?
Tokenization is an essential IT security strategy for protecting sensitive data such as credit card numbers by replacing them with secure “tokens.” Inspired by the simple idea of exchanging a valuable object for a symbolic token with no own value, this method makes it possible to secure critical information while allowing it to be used in current transactions or verifications without risk of disclosure. Tokenization is based on the concept of transforming sensitive information — for example, a card number — into a sequence of random characters (the token), which contains no usable data outside the secure system that knows how to convert it back into the original information.
The generation of these tokens is done via algorithms that create a unique correspondence between the token and the real information, stored in an extremely secure environment. In practice, tokenization thus contributes to the secure management of payments and personal data, substantially reducing the risks of fraud while allowing businesses to conduct their online activities securely.
What is anonymization?
According to the CNIL, anonymization consists in applying various techniques to make any identification of a person impossible and irreversible, whether directly or indirectly, through the remaining data. This process changes or processes personal data so that it becomes anonymous. This can include masking, adding noise, or even removing identifying information, thereby preventing the disclosure of personally sensitive information while allowing datasets to be exploited for analysis, research, and other applications.
The need for anonymization is part of a desire to protect the privacy of individuals in a context where the collection and use of data are omnipresent.
Octopize's approach to anonymization, called the avatar method, highlights the importance of advanced techniques to ensure data privacy in today's digital landscape, while complying with strict regulatory guidelines.
Tokenization Vs. Anonymization
Understanding the differences between tokenization and anonymization is essential in the current context of user data protection. While the two approaches are meant to secure sensitive information, they operate in distinct ways and meet specific needs.
Anonymization, as defined by the CNIL and practiced at Octopize, consists in applying a set of techniques to make any attempt to identify the person concerned, direct or indirect, practically impossible and irreversibly impossible. This operation aims at the ultimate protection of privacy by allowing the use of data for analysis and research purposes while complying with current data protection regulations.
In contrast, tokenization replaces sensitive information with a unique and secure token, maintaining a link to the original data stored in a highly secure environment. This process is reversible, unlike anonymization, and is therefore considered a form of pseudonymization. The generated tokens allow sensitive data to be used and manipulated in operational contexts without exposing original details, thus limiting the risk of fraud. However, tokenization, by maintaining a cryptographic link to the initial information, does not guarantee complete anonymity.
In summary, while anonymization irreversibly erases any possibility of identifying an individual from the data processed, tokenization offers protection by replacing sensitive data with tokens, without cutting the link with the original information. This distinction is crucial for organizations looking to optimize data security by choosing the approach that best fits their specific needs and regulatory requirements.
Use cases
Tokenization
The analogy of the concert ticket illustrates this idea well: instead of risking losing or having a precious ticket stolen, we entrust it to a safe, in return getting a representative token that proves its existence without directly exposing it. In a digital context, this means that when an online transaction is carried out, the real card number is never stored or transmitted to the merchant's server; only the token circulates, making any attempt to steal data by hackers useless, because without the key to the tokenization system, these tokens are of no use to them.
Anonymization
A pharmaceutical laboratory, having finalized a clinical study for a new treatment for humans, collected sensitive data from its patients to be able to confirm the added value of this treatment. These sensitive data, such as age, gender, or others, are decisive for the effectiveness of the treatment. So, although it is sensitive data, it is of great statistical importance.
This laboratory would like to use sharing this data with another partner outside Europe, for the creation of another treatment. Without anonymization, this is not possible, because of the legal framework of the RGPD limiting the transfer of personal data of European citizens outside Europe.
Thus, we can apply anonymization techniques such as the Octopize avatar method on this dataset, to keep the statistical properties decisive for future studies, and remain within the legal framework imposed by the RGP, because anonymous data is no longer considered personal data.
Conclusion
In conclusion, in a world where the value of data is constantly growing and the risks of cyberattacks are increasing proportionally, understanding and effectively applying techniques such as tokenization and anonymization has become crucial for businesses, regardless of the sector.
Tokenization, offering a form of pseudonymization by substituting sensitive data by secure tokens, and anonymization, aimed at making the identification of individuals from the data processed completely irreversible, are two complementary methods for ensuring the security of users' personal information.
The complementarity of these technologies offers companies a range of tools that not only allow them to comply with strict data protection regulations, such as the GDPR in Europe, but also to maintain the trust of users by guaranteeing the integrity and confidentiality of their information. However, each approach has its own specificities and must be chosen after a thorough analysis of the company's needs and the legal requirements in force.
Innovation in personal data protection continues to evolve, and with the introduction of advanced techniques such as the avatar method, Octopize is positioned at the forefront of data security.
Businesses looking to strengthen their data protection strategy, while aligning themselves with the latest technological advances, can consider Octopize as a strategic partner.
Written by Lucas Sehairi & Tom Crasset
Sources:
- https://www.lesdatalistes.fr/article/actualite-2024-marche-data-marketing#:~:text=Une%20progression%20attendue%20de%20l,4%25%20d'ici%202026.
- https://cyber.gouv.fr/actualites/lanssi-publie-le-panorama-de-la-cybermenace-2023
- https://www.group-ib.com/resources/research-hub/hi-tech-crime-trends-2023-eu/ (https://www.prnewswire.com/fr/communiques-de-presse/group-ib-devoile-les-tendances-cyber-la-france-est-le-pays-le-plus-touche-en-europe-par-les-fuites-de-donnees-les-attaques-de-ransomwares-augmentant-de-45--en-2023-302073915.html)
