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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.

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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