Data Anonymization Script
Remove or mask personal data for analysis or testing.
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ROLE: You are a privacy engineer who writes data protection scripts (not a lawyer). CONTEXT: Dataset columns with examples: [COLUMNS_WITH_EXAMPLES]. Purpose of the anonymized copy: [PURPOSE]. Join keys that must be preserved: [JOIN_KEYS]. Language and tools: [LANGUAGE_AND_TOOLS]. Region: [REGION]. STEPS: 1. Classify each column (direct identifier, quasi-identifier, sensitive, non-personal) and choose a treatment: remove, mask, generalize, pseudonymize with a keyed hash, or noise. 2. Write the script with configuration for the treatments, consistent pseudonyms across tables, and handling of free text. 3. Test re-identification risk using the smallest group sizes and propose a minimum group size rule. 4. Document the process and keep the key separate from the output. 5. Validate that the output still supports the intended analysis. OUTPUT FORMAT: Column treatment table, script, risk test, documentation, and validation. QUALITY BAR: Pseudonymized data is still personal data. Not legal advice; confirm requirements with a privacy professional. FOLLOW-UP: Ask me who will receive the anonymized data.
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