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Guide to Data Anonymization

Companies are often stuck with outdated legacy anonymization techniques. However, these old data anonymization tools not only destroy data utility but also endanger privacy.

Upgrading your data anonymization toolset to a synthetic data generator brings many advantages. Privacy-by-design is only one of them.

The data anonymization guide introduces common legacy anonymization techniques and how they compare to synthetic data. The guide discusses the following data anonymization tools:

  • data masking,
  • pseudonymization,
  • encryption,
  • randomization,
  • permutation,
  • generalization.

In this ebook, you'll not only learn why these tools are decreasing data utility and increasing privacy risks, but also find recommendations for replacing them state-of-the-art privacy-enhancing technologies, like homomorphic encryption, federated learning, secure multi-party computation, differential privacy and AI-generated synthetic data.

Upgrading your data anonymization toolset to a synthetic data generator brings many advantages. Privacy-by-design is only one of them.

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