The challenges of data anonymisation in the company

Data anonymization is an essential approach to enable companies to exploit personal data while ensuring compliance with data protection regulations, notably the GDPR. The aim is to transform or adapt datasets to prevent any direct or indirect identification of individuals. Although this approach presents many opportunities for companies, it must meet strict requirements in terms of cybersecurity and GDPR compliance.

A strict regulatory framework for data protection

Today, the use of personal data is very strictly regulated. The General Data Protection Regulation (GDPR) requires every individual to have control over how their personal information is used.

Obligations include:

• Informing individuals when data is collected.

• The identification, documentation and monitoring of data processing.

• Compliance with the principles of data minimization and purpose.

• Failure to comply with these requirements exposes companies to severe sanctions from regulatory authorities, notably the CNIL in France.

Why make data anonymous?

To reconcile data exploitation and privacy protection, companies are increasingly adopting anonymization solutions. The aim is to transform a dataset in such a way that it is impossible to identify an individual, while retaining analytical value for uses such as :

• Big Data analysis

• Artificial intelligence and machine learning algorithm training

• Software testing and fraud detection

However, database anonymization is a complex process requiring a rigorous approach.

Direct and indirect identification: a real challenge

To guarantee effective anonymization, it is essential to prevent any identification of individuals, even by cross-referencing data. An anonymized dataset must not allow :

• Individualization: If only the identity (surname, first name) is modified, an individual can still be identified by his or her background or unique characteristics.
• Correlation of data sets: The association of several databases (e.g. medical prescriptions + pharmacy purchases) can enable a person to be traced.
• Geolocation data: inferred information, such as a recurring commute, can reveal a person's identity.

The difference between pseudonymization and anonymization

Pseudonymization and anonymization are often confused, but they do not offer the same level of protection:

• Pseudonymization replaces certain data (e.g. name with a random identifier), but still allows re-identification by cross-referencing information.
• Anonymization removes any possibility of re-associating data with an individual.

What are the anonymization techniques?

Anonymization is achieved using clearly identified techniques. These include the following:

• Deletion: elimination of unnecessary data.
• Generalization: dilution of data (e.g. reducing a date of birth to an age range).
• Substitution: replacement of values (e.g. one first name by another).
• Randomization: random modification of data while maintaining statistical consistency.
• K-anonymization: grouping of individuals to prevent their identification.
• Encryption and hashing: cryptographic transformation of data.

In order to guarantee data anonymization, several of these techniques will usually need to be implemented.

The approach must consider the context

In particular, the intended use of the data should determine the anonymization project to be implemented.

In this case, only the data required for processing will be kept, before considering the best approach to ensure that the data is relevant and useful for the intended purpose, while guaranteeing that no individual can be identified.

Data requirements are not the same if the aim is to use a dataset to carry out tests, to train a model or to identify fraud attempts.

A multi-step process

Effective anonymization is based on a multi-stage strategy, involving DPOs (Délégués à la Protection des Données) and data science experts:

1. Identification of sensitive data.
2. Selection of anonymization techniques according to data usage.
3. Application of selected methods (e.g. generalization, deletion, pseudonymization).
4. Verification of non-identification of individuals.
5. Regular monitoring and compliance audits.

Data anonymization is a crucial issue for companies handling sensitive data. It makes it possible to reconcile data exploitation and GDPR compliance while reducing the risks associated with data leaks and cyberattacks. Implementing an effective anonymization strategy is therefore essential to ensure the confidentiality of personal data and guarantee legal compliance.

Need help to anonymize your data?

Anonymization is a complex process requiring advanced expertise in cybersecurity and data protection. Our experts can work with you to ensure your company's compliance and the security of your data. Get in touch with a DEEP cybersecurity expert to discuss your needs!