Guides ยท Business
Data Anonymization Basics
Reduce identifiability in data
Effective anonymization drops direct identifiers, generalizes or buckets quasi-identifiers, adds noise where needed, and assesses re-identification risk before sharing or analysis.
- anonymization
- k-anonymity
- privacy
- data sharing
- de-identification
Strip direct identifiers
Remove names, emails, IDs; replace with randomized tokens.
Handle quasi-identifiers
Generalize or bucket traits like age, zip, and dates to reduce uniqueness.
Limit linkage risk
Drop unnecessary columns and reduce granularity where possible.
Test anonymization
Assess k-anonymity or re-ID risk with sample attacks; document limits.
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