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Anonymized data

Anonymized data is a type of data that has been processed to remove any personally identifiable information (PII) or personal data. Such data is often used in research, analytics, and other data-driven activities, as well as for compliance with privacy regulations.

According to GDPR, anonymized data has been altered in such a way that it can’t be used to identify a specific person. Since anonymized data can’t be restored, it isn’t considered personal data under GDPR. This means it is exempt from GDPR.

Some examples of compliant data anonymization methods include:

  • Randomization:
  1. Noise addition – Where personal identifiers are expressed imprecisely, for instance: height: 180 cm → height 320 cm
  2. Substitution – Where personal identifiers are shuffled within a table or replaced with random values, for instance: ZIP: 10120 → ZIP: postcode
  • Generalization:
  1. Aggregation – Where personal identifiers are generalized into a range or group, for instance: age: 30 → age: 20-35

Removing any identifiable information from a dataset allows for meaningful analysis without compromising the privacy of individuals.

Examples of use cases for anonymized data include:

  • Measuring the effectiveness of marketing campaigns.
  • Analyzing the behavior of website or mobile app users.
  • Analyzing trends and patterns.

Further reading:

  • 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.

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