Skip to content
English
Incident Support Customer Portal
Contact Us
  • There are no suggestions because the search field is empty.

How do I Anonymise Personal Data?

Anonymising personal data is an essential practice for protecting individuals' privacy and complying with data protection regulations like the GDPR. Anonymisation is the process of removing or modifying personal information so that individuals cannot be identified from the data, either directly or indirectly, without additional information that is kept separately.

Here’s a step-by-step guide to anonymising personal data effectively:

🔹 Key Steps to Anonymise Personal Data:

1. Understand What Constitutes Personal Data

Before anonymising, it’s crucial to identify personal data that needs to be anonymised. Personal data can include:

  • Names, addresses, emails, and phone numbers.
  • Identifiers like social security numbers, IP addresses, and biometric data.
  • Indirect identifiers (e.g., job titles, gender, or dates of birth) that, when combined, can identify an individual.

If any of these are included in your data set, it is considered personal data and needs protection.

2. Define the Anonymisation Objective

Determine your objective for anonymisation. Are you:

  • Making the data completely untraceable to an individual?
  • Allowing the use of data for analytics or research while preserving privacy?
  • Complying with regulatory requirements (e.g., GDPR) or industry standards (e.g., HIPAA)?

Your strategy for anonymisation will depend on these objectives.

3. Choose the Right Anonymisation Techniques

There are several methods to anonymise personal data. Here are the most common and effective techniques:

A. Data Masking

  • Masking replaces real values with fake ones (e.g., replacing the actual name with a placeholder, such as "John Doe").
  • The original data is preserved in a hidden form, making it difficult for unauthorized persons to identify individuals.

B. Pseudonymisation

  • Pseudonymisation replaces identifiable fields (such as names or IDs) with pseudonyms or codes that cannot be directly attributed to an individual without additional information.
  • Example: Instead of using "John Smith," you might use a code like "ID12345." Only authorized users with access to the mapping table can re-identify the person.

C. Data Aggregation

  • Aggregation involves grouping personal data into categories or ranges, which makes it difficult to identify individuals.
  • Example: Instead of showing individual ages, you might display age ranges like 20-30, 31-40, etc.
  • This technique is especially useful when using the data for statistical analysis.

D. Generalisation

  • Generalization reduces the precision of data to make it less identifiable.
  • Example: Instead of recording someone's exact age (e.g., 29), generalize it to a range (e.g., 20-30 years old).
  • This technique is useful for ensuring individuals cannot be pinpointed.

E. Data Perturbation

  • Perturbation involves altering the data slightly so that the overall analysis remains valid, but the data is less accurate for identifying individuals.
  • Example: Slightly adjusting numbers or values in a dataset, so they are not exactly the same as the original but still provide useful aggregate data.

F. Differential Privacy

  • Differential privacy adds noise to datasets in such a way that the data remains useful for analysis, but the privacy of individuals is maintained.
  • This technique is often used in machine learning and statistical analysis to prevent the extraction of personal information from aggregated datasets.

4. Ensure Proper Data Retention and Destruction

After anonymising the data, you should establish data retention and destruction policies. This involves:

  • Limiting data retention: Anonymised data should only be kept for as long as necessary for the specified purposes.
  • Secure destruction: Once the data is no longer required, ensure that the anonymised data is securely destroyed, preventing any possibility of re-identification.

5. Maintain Separate Data Sets

To ensure effective anonymisation, you must keep identifiers and anonymised data in separate locations. If both sets are stored together or accessible by unauthorized parties, re-identification may be possible.

  • Ensure that the key mapping anonymised data to identifiable data (in case pseudonymisation was used) is securely stored and access-controlled.

6. Use Encryption and Secure Access Controls

If your anonymised data is sensitive or requires additional layers of protection, implement encryption and access control measures:

  • Encrypt data both at rest and in transit to further protect it from unauthorized access.
  • Limit access to only authorized personnel to prevent any accidental exposure of personal data.

7. Verify the Anonymisation Process

Once you’ve anonymised your data, ensure that it cannot be re-identified, especially when combined with other datasets. Some practices to verify anonymisation include:

  • Re-identification testing: Regularly test if it’s possible to re-identify individuals using the anonymised data and check for any weaknesses in your anonymisation process.
  • Third-party audits: Consider having external experts conduct audits to verify that your data anonymisation techniques are effective.

8. Comply with Data Protection Regulations

Ensure that your anonymisation efforts comply with relevant data protection laws, including:

  • GDPR (General Data Protection Regulation): Anonymised data is not considered personal data under the GDPR, but you must ensure that re-identification is not possible without additional data. Pseudonymisation is encouraged by GDPR as a safeguard measure.
  • HIPAA (Health Insurance Portability and Accountability Act): Under HIPAA, health data must be de-identified, and anonymisation is one way to achieve this.
  • CCPA (California Consumer Privacy Act): Anonymised data may be exempt from certain CCPA provisions, but organizations must ensure it cannot be re-identified.

🔹 Best Practices for Data Anonymisation:

  1. Minimize Data Collection: Only collect data that is absolutely necessary for the task at hand.
  2. Use Anonymisation Before Sharing: Always anonymise data before sharing it with third parties, including service providers and research partners.
  3. Regularly Audit Anonymised Data: Conduct regular audits of anonymised data to ensure that no personal data can be re-identified.
  4. Apply a Layered Approach: Consider combining multiple anonymisation techniques (e.g., masking and aggregation) for a stronger privacy safeguard.

🔹 Tools for Data Anonymisation:

  • ARX Data Anonymisation Tool: A comprehensive open-source tool for anonymising sensitive data while preserving its utility.
  • Anonymizer.io: A tool for creating pseudonymous data in compliance with privacy regulations.
  • Data Masker for SQL Server: A commercial tool for masking sensitive data in SQL databases.
  • Privacy Tools: Tools like HPrivacy offer a combination of anonymisation and privacy-preserving analysis.