Data Anonymization Techniques for Privacy

Act as a data privacy consultant and outline various data anonymization techniques that can be applied to a dataset to ensure privacy while preserving its usability for analytical purposes. Consider methods such as data masking, pseudonymization, data generalization, and differential privacy. Discuss the advantages and limitations of each technique, provide examples of use cases, and suggest scenarios where one method might be preferred over others. Additionally, explain any potential impacts on data analysis accuracy and how to mitigate these effects.