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Data Anonymization Patterns(DAP)
Comprehensive data anonymization techniques including K-anonymity, L-diversity, T-closeness, and synthetic data generation for agentic systems
In 30 seconds
- What
- Removes or transforms personally identifiable data so individuals cannot be re-identified, using techniques like generalization, suppression, and synthetic data generation.
- When to use
- Sharing datasets across organizations or agents where privacy regulations apply and you need to prevent linking records back to individuals.
- Watch out
- Aggressive anonymization severely degrades data utility; attackers can still re-identify through auxiliary information or linkage attacks.
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Data Anonymization Patterns: Overview
Comprehensive data anonymization techniques including K-anonymity, L-diversity, T-closeness, and synthetic data generation for agentic systems
- K-anonymity for indistinguishability
- L-diversity for sensitive attribute protection
- T-closeness for distribution similarity
- Synthetic data generation
- Federated anonymization processing
- Privacy-utility trade-off optimization
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References
The papers, specifications, and repositories this pattern is based on.
- K-Anonymity Model (Sweeney, 2002)
- Federated Learning Privacy (Liu et al., 2020)
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