What Techniques are Commonly Used for Data Anonymization?

Several techniques are employed to anonymize data effectively. These include:

Data Masking: Replacing identifiable information with pseudonyms or random characters.
Generalization: Reducing the specificity of data to make it less identifiable, for example, using age ranges instead of exact ages.
Perturbation: Adding noise to the data to obscure individual identities while retaining overall data trends.
K-anonymity: Ensuring that each record is indistinguishable from at least k-1 other records in the dataset.
Differential Privacy: Introducing random noise to the data queries to protect individual data points while allowing statistical analysis.