# Differential Privacy in Production — Workflows

## Workflow 1: DP Analytics Pipeline Deployment

### Steps
1. **Use Case Assessment**: Determine if DP is appropriate for the analytics use case
2. **Sensitivity Analysis**: Calculate global sensitivity for target queries
3. **Epsilon Selection**: Choose epsilon based on data sensitivity and utility needs
4. **Mechanism Selection**: Select Laplace, Gaussian, or Exponential mechanism
5. **Budget Allocation**: Set total privacy budget and per-query allocations
6. **Implementation**: Integrate DP mechanism into analytics pipeline
7. **Utility Testing**: Validate query accuracy meets business requirements
8. **Budget Monitoring**: Deploy privacy budget tracking and alerting
9. **Documentation**: Record epsilon choices, composition method, and rationale

## Workflow 2: DP Model Training (DP-SGD)

### Steps
1. **Model Selection**: Choose model architecture (affects gradient dimensions)
2. **Clipping Norm**: Determine per-example gradient clipping norm (C)
3. **Noise Multiplier**: Set Gaussian noise multiplier (sigma)
4. **Sampling Rate**: Configure Poisson subsampling rate
5. **Privacy Accounting**: Initialize moments accountant or RDP accountant
6. **Training**: Train with DP-SGD, monitoring privacy budget consumption
7. **Privacy Statement**: Report final (epsilon, delta) guarantee
8. **Utility Evaluation**: Compare model accuracy with non-private baseline

## Workflow 3: Privacy Budget Management

### Steps
1. **Budget Setting**: Define total (epsilon, delta) budget per dataset per time period
2. **Composition Selection**: Choose composition theorem (basic, advanced, RDP)
3. **Allocation Policy**: Define per-query budget allocation rules
4. **Access Control**: Integrate budget checks with query execution
5. **Monitoring**: Track budget consumption and forecast exhaustion
6. **Alerting**: Alert when budget approaches threshold (e.g., 80%)
7. **Renewal**: Establish budget renewal policy (annual, per data refresh)