Privacy-preserving data mining (PPDM) is a broader concept that includes data anonymization as one of its techniques. PPDM focuses on extracting useful information from large datasets without compromising the privacy of the individuals involved. Data anonymization plays a crucial role in PPDM by ensuring that the data can be analyzed and shared without revealing personal information.

Pseudonymization and tokenization are specific techniques used in the process of data anonymization. Pseudonymization replaces private identifiers with fake identifiers or pseudonyms, making it difficult to trace data back to an individual. Tokenization, on the other hand, replaces sensitive data elements with non-sensitive equivalents called tokens, which can be mapped back to the original data only through a secure tokenization system.

Data masking and data scrambling are techniques that alter the original data to protect sensitive information. Data masking changes data in a way that it remains usable for analysis but hides the actual values. Data scrambling, a similar concept, involves rearranging or randomizing data elements to prevent identification.

Differential privacy is a mathematical approach to data anonymization that adds random noise to datasets in a controlled manner. This ensures that the privacy of individuals is maintained while still allowing for accurate statistical analysis. It provides strong privacy guarantees and is widely used in research and industry.

Decentralized learning and geo masking are advanced techniques related to data anonymization. Decentralized learning, such as federated learning, involves training machine learning models on decentralized data sources without sharing the actual data. Geo masking, on the other hand, involves altering geographic data to protect the location privacy of individuals. Both techniques contribute to the broader goal of data privacy and security.