Agentic Design

Patterns
🎭

Data Anonymization Patterns(DAP)

Comprehensive data anonymization techniques including K-anonymity, L-diversity, T-closeness, and synthetic data generation for agentic systems

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Comprehensive data anonymization techniques including K-anonymity, L-diversity, T-closeness, and synthetic data generation for agentic systems

Why: Protects individual privacy, enables safe data sharing, supports federated learning, and ensures regulatory compliance

Key Insight: Multi-layered anonymization + synthetic generation + federated processing → privacy-preserving agent collaboration

⚡ Quick Implementation

1Data Classification:Identify PII, quasi-identifiers, sensitive attributes
2Anonymization Method:K-anonymity, L-diversity, T-closeness selection
3Synthetic Generation:GAN-based or statistical synthetic data
4Federated Processing:Distributed anonymization across agents
5Utility Validation:Privacy-utility trade-off assessment
Example: data_classification → anonymization_method → synthetic_generation → federated_processing → utility_validation

📋 Do's & Don'ts

✅Implement k-anonymity with minimum k=5 for basic protection
✅Use synthetic data generation for high-risk PII scenarios
✅Apply federated anonymization for multi-agent environments
✅Combine multiple techniques (L-diversity + T-closeness)
✅Regular re-identification risk assessment and testing
❌Rely solely on data masking without anonymization models
❌Use simple generalization that destroys data utility
❌Ignore quasi-identifier combinations for re-identification
❌Apply anonymization after data has been widely distributed
❌Skip validation of synthetic data statistical properties

🚦 When to Use

Use When

  • • Multi-agent federated learning
  • • Cross-organizational data sharing
  • • Public dataset publication
  • • Regulatory compliance requirements

Avoid When

  • • Already encrypted data at rest
  • • Internal single-agent processing
  • • Public domain datasets
  • • Real-time streaming requirements

📊 Key Metrics

Re-identification Risk
Probability of individual identification
Data Utility Preservation
% statistical accuracy maintained
K-Anonymity Level
Minimum group size for indistinguishability
L-Diversity Score
Sensitive attribute value diversity
T-Closeness Threshold
Distribution similarity to population
Synthetic Data Fidelity
Statistical similarity to original data

💡 Top Use Cases

Healthcare AI: Patient record anonymization for multi-hospital federated learning
Financial Agents: Transaction pattern analysis with customer privacy protection
Smart City: Citizen mobility data sharing across government agencies
Research Collaboration: Academic dataset sharing with privacy guarantees
Insurance Analytics: Claims data anonymization for regulatory compliance

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

K-Anonymity: A Model for Protecting Privacy (Sweeney, 2002)

L-Diversity: Privacy Beyond K-Anonymity (Machanavajjhala et al., 2007)

T-Closeness: Privacy Beyond K-Anonymity and L-Diversity (Li et al., 2007)

Anonymizing Data for Privacy-Preserving Federated Learning (Liu et al., 2020)

Implementation Guides

Google AI - Federated Learning for Mobile Keyboard Prediction

Microsoft Presidio - Data Protection and Anonymization

IBM Federated Learning Framework

AWS Comprehend - PII Detection and Redaction

Tools & Libraries

ARX - Open Source Data Anonymization Tool

Amnesia - K-Anonymity Tool

DataSynthesizer - Synthetic Data Generation

SDV (Synthetic Data Vault) - Python Library

Community & Discussions

International Association of Privacy Professionals (IAPP)

Privacy Engineering Research at CMU

OpenMined - Privacy-Preserving AI Community

NIST Privacy Engineering Program

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Loading...