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By definition, data anonymization is information sanitization for privacy protection. It is the process of removing personally identifiable information from data sets so that the people whom the data describe remain anonymous.
So anonymized data is a type of information sanitization. When anonymization tools encrypt or remove personally identifiable information from datasets to preserve a data subject’s privacy, this will reduce the risk of unintended exposure during the transfer of information across boundaries and facilitate evaluation post-anonymization.
The European Union’s General Data Protection Regulation (GDPR) requires the anonymization of stored data on people located in the EU. However, as anonymized data sets are no longer deemed personal information, it is not subject to the GDPR.
How is data anonymization done?
To ensure proper data anonymization, first, you need to assess the complexity of the project and the programming language used. Data anonymization software usually provides compliance with GDPR anonymized data. There are some techniques for data anonymization, which include the following six methods:
Generalization: this method eliminates only some parts of the data and retains accuracy
Perturbation: generally, add a way for rounding up numbers
Pseudonymization: replaces identifiers with fake or pseudonyms
Scrambling: mostly scrambles letters, mixes or rearranges them
Shuffling: as the name indicates, data shuffling is swapping datasets
Synthetic data: manufactures artificial algorithmically datasets
When we talk about data anonymization, we talk about user information security.
Information security is a critical topic, and dealing with it is even more critical. The advantage of using anonymization as a technique for information security is implementing it during data collection. However, this method has its drawbacks: reducing data utility when personalizing user experiences or originating value insight from your data.