The document outlines various data anonymization techniques aimed at replacing sensitive information while preserving its utility for analysis. Techniques include data masking, generalization, data swapping, pseudonymization, data perturbation, and synthetic data generation. Each technique provides examples to illustrate how sensitive data can be modified or replaced to maintain privacy during testing or analysis.

1. DATA ANONYMIZATION
TOP
TECHNIQUES

2. Replaces sensitive data with modified values for testing/analysis while
retaining format.
Example: Random digits replace credit card numbers.
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DATA MASKING

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Broaden data categories to reduce specificity.
Example: Exact ages are replaced with age ranges
(e.g., 20-30).
GENERALIZATION

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Shuffles data within a dataset to disrupt individual associations while
maintaining distribution.
Example: Swap employee salaries without changing
departments.
DATA SWAPPING

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Replaces private identifiers with pseudonyms, allowing re-identification if
needed.
Example: Patient names are replaced with codes,
accessible with decryption.
PSEUDONYMIZATION

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Slightly modifies data values to protect individual identities while
preserving statistical accuracy.
Example: Add random values to income data.
DATA PERTURBATION

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Creates new data with characteristics similar to the original dataset but
unrelated to real individuals.
Example: Generate similar customer data without real
information.
SYNTHETIC DATA

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