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Data anonymization

Data anonymization is the use of one or more techniques designed to make it impossible to identify a particular individual from stored data related to them. That kind of data bring significant benefits to businesses collecting data since anonymous data is not personal data for the purposes of GDPR.

Anonymous data doesn’t require any additional safeguards to ensure its security. Among other things, this means that, you don’t need to get consent to process it and it can be exported internationally.

In terms of GDPR we can anonymize data such as:

  • login details
  • device IDs
  • IP addresses
  • cookies
  • device type
  • language preference
  • time zones

Example methods of data anonymization:

  • Noise Addition – example: height: 180 cm → height: 320 cm
  • Aggregation – example: Age: 30 → Age: 20-35
  • Substitution – example: : 10120 → ZIP: postcode

To get more details on data anonymization, read the Piwik PRO blog:

  • Privacy by design in practice: How “just enough” data beats “just in case” collection

    While collecting more data “just in case” feels safer, according to Matt Gershoff, it’s also one of the biggest sources of unnecessary compliance risk, analytical noise, and wasted organizational resources in the analytics industry today. His approach of “just enough” data collection is more intentional, more aligned with privacy regulation, and often more analytically effective.

  • 4 ways to make your analytics HIPAA-compliant: Implementation guide

    Healthcare organizations have four main approaches to achieving HIPAA-compliant analytics. Each has different trade-offs in cost, technical complexity, and analytics capabilities. This guide compares all four implementation methods – from using Google Analytics with workarounds to deploying fully HIPAA-compliant analytics platforms – so you can choose the right approach for your organization’s needs and resources.

Other definitions

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