Safe handling of sensitive data in your platform: anonymisation and pseudonymisation explained

What does automatic anonymisation mean?

Automatic anonymisation detects sensitive information in all files within your digital information platform and proceeds to generalise this data by replacing it with generic data. Alternatively, the information can be omitted and thus randomised. In both cases, this happens during the transformation process. This keeps the data usable but the original source is anonymous when you consult the file. This ensures that your information can be accessed securely without revealing unnecessarily sensitive details.

What is Pseudonymisation?

Pseudonymisation involves encrypting sensitive information and replacing it with a new value. This ensures that the direct link to the original data is broken. Think, for example, of a username being replaced by a unique identifier, such as a code or ID. Unlike anonymisation, pseudonymisation makes it possible, with the right additional information, to retrieve the original data.

When do you use anynomisation and pseudonimisation?

The choice between anonymisation and pseudonymisation depends on the specific needs of your organisation. Anonymisation offers a higher level of privacy protection, while pseudonymisation can be useful when it is important to maintain a link to the original data, for example for research purposes. With pseudonymisation, you basically have the option of reversibility and restoring the original values. With anonymisation, this is not possible.

The benefits of anonymisation and pseudonymisation

• Protection of privacy: Individuals' personal data are protected. This reduces the risk of privacy breaches. This is crucial when working with sensitive information such as customer or employee data. 
  Use case: An e-commerce company anonymises customer data such as names and credit card information before sending it to a data warehouse. This protects them from data breaches in analytics or cyber attacks.. 
  
• Regulatory compliance: Anonymisation and pseudonymisation support your compliance with privacy laws and regulations such as the GDPR. As an organisation, it helps you avoid possible fines and legal issues.  tuele boetes en juridische problemen te voorkomen.   
  Use case: A healthcare organisation pseudonymises patient data before sharing it with a research agency for medical research. Only with additional information can the original identity be traced.
  
• Secure data sharing: When collaborating with third parties, such as consultants or suppliers, customer data can be pseudonymised. This keeps core information usable, but without personal data being directly traceable.
  Use case: An insurance company shares anonymised claims data with an external data analyst to detect fraud patterns.
  
• Flexibility in your data: Want to extract more insights from your information? Thanks to anonymisation and pseudonymisation, you can safely work with all the information within your platform. This allows you to extract more value from your wealth of information with confidence.  
  Use case: A retail company anonymises checkout data from thousands of customers to analyse buying trends and optimise stock management.

Conclusion

By using anonymisation and pseudonymisation strategically, companies can leverage data without compromising customer security or privacy. It is the perfect balance between compliance and value creation.

Not sure what exactly you need for your specific use case? Our team of experienced consultants will analyse your business and select the best toolset together with you. This is how we help support your organisation in the best possible way!