What are the different methods of data anonymization?
There are several methods of data anonymization, including data masking, pseudonymization, and generalization. Would you like to know more about a specific method?
Can you explain data masking?
Data masking involves altering data to protect sensitive information while maintaining its usability. For example, replacing real names with fictitious ones.
What about pseudonymization?
Pseudonymization replaces private identifiers with fake identifiers or pseudonyms. This allows data to be matched with its source without revealing the actual identity.
Are there any drawbacks to these methods?
Yes, while they enhance privacy, they can also reduce data utility and may not fully protect against re-identification if not implemented properly.
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