The data are an essential resource, the anonymization has become a critical tool to protect individual privacy and to comply with privacy regulations such as the RGPD (General Regulation on Data Protection). This process transforms personal data in such a way that cannot be identified to an individual, even combining multiple data sets. Below, we describe the main techniques of anonymity and their applications:

1. Data masking

Masking replaces sensitive data with fictitious characters or irrelevant, as names are converted into pseudonyms or numbers replaced by asterisks.

• Example: To transform the card number “1234-5678-9012-3456” in “–-****-3456″.
• Use: Software testing and development where the actual data are not required.

2. Pseudonimización

This technique replaces the data identified by identifiers artificial means, such as alphanumeric codes. Unlike the anonymization total, the original data can be retrieved with a secure key.

• Example: Change “John Doe” by “Usuario12345”.
• Application: Medical research and internal analysis with additional measures to prevent re-identification.

3. Generalization

The generalization reduces the accuracy of the data, replacing specific values with ranges or wider categories.

• Example: Convert the old “29 years” in the range “20-30 years”.
• Benefit: Minimizes the risk of re-identification to limit personal details.

4. Disturbance

Is to modify the data by adding statistical noise. This method alters slightly the values without affecting the general patterns.

• Example: Modify an income of $50,000 to $50,032.
• Common uses: Analysis of demographic data or financial where exact precision is not crucial.

5. Suppression

Completely deletes certain data or records of a set to protect identities.

• Example: Delete the name of the participants in a public opinion poll.
• Application: Publication of results where you do not need specific data.

6. Swapping (Exchanging Data)

Change values between records to break direct links between the data and the people.

• Example: Exchanging zip codes between different records.
• Benefit: Maintains global statistics while protecting privacy.

7. Tokenization

Becomes sensitive data in tokens, or representations that have no intrinsic value.

• Example: To transform the bank account number in a token like “abc123xyz”.
• Use: Financial transaction and payment processing.

Considerations when Choosing a Technique

The selection of the technique depends on:

• Purpose of data: Analysis, sharing, or tests.
• Sensitivity level: Data highly sensitive methods require more robust.
• Regulations: Comply with regulations as RGPD, or CCPA.

The anonymization effective not only protects individuals, but also allows companies to take advantage of the data of legal and ethical manner. However, it is essential to continually review the methods used, as technological advances such as artificial intelligence pose new challenges in the re-identification of data anonymised.

By implementing these techniques, organizations can ensure the privacy, build confidence, and to stay ahead in the responsible handling of information.

If you need help with this or other topics to implement greater protection of the data that drives your company, contact us to schedule a meeting totally free with our experts.