Data Anonymization Techniques

Data anonymization techniques are methods used to protect sensitive information in datasets. These techniques make it impossible to link the data to an individual, thereby protecting their privacy. There are several data anonymization techniques, including data masking, data perturbation, and data suppression. Data masking involves replacing sensitive data with fictional data, while data perturbation involves adding noise to the data to make it more difficult to identify individuals. Data suppression involves removing sensitive data from the dataset altogether.